Package 'Ecdat'

Description

a cross-section

number of observations : 40

Usage

data(Accident)

Format

A dataframe containing :

type

ship type, a factor with levels (A,B,C,D,E)

constr

year constructed, a factor with levels (C6064,C6569,C7074,C7579)

operate

year operated, a factor with levels (O6074,O7579)

months

measure of service amount

acc

accidents

Source

McCullagh, P. and J. Nelder (1983) Generalized Linear Models, New York:Chapman and Hall.

References

Greene, W.H. (2003) Econometric Analysis, Prentice Hall, https://archive.org/details/econometricanaly0000gree_f4x3, Table F21.3.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

Accountants and auditors as a percent of the US labor force 1850 to 2016 updating the analysis in Wyatt and Hecker (2006).

Usage

data(AccountantsAuditorsPct)

Format

a numeric vector of length 30 giving the percent of the US labor force in "Accounting and Auditing" each decade from 1850 to 2010 except for 1940 plus each year between 2011 and 2016.

Source

This is based primarily on data extracted from the Integrated Public Use Microdata Series on 2018-09-01 with the computations documented in a vignette by this title in the Ecfun package.

This updates the data on Accountants and Auditors in Wyatt and Hecker (2006). They relied primarily on data extracted from the Integrated Public Use Microdata Series. This follows the same methodology with two modifications:

1. IPUMS provided no data for 1940. Wyatt and Hecker (2006) used Historical Statistics of the United States, Colonial Times to 1970, Bicentennial Edition, part 1 (U.S. Department of Commerce, Bureau of the Census, 1975) for 1910-1940. The current data set uses that source only for 1040.

2. The IPUMS numbers showed an extreme jump from 1850 to 1860 followed by an even more extreme drop to 1870. The numbers in Sobek (2006) showed essentially the same behavior. Specifically, Sobek (2006) estimated the number of accountants and auditors in the US in those three years as 700, 1700, and 1200, and the labor force as 5277000, 8160800, and 12004200. These numbers give accountants and auditors as 0.013, 0.021, and 0.010 percent of the labor force, respectively for those three years. These numbers portray an incredible increase in the employment of accountants and auditors between 1850 and 1860 followed by a shocking decline the following decade. If, however, we swap the 1700 and 1200 between 1860 and 1870, the percentages become quite stable: 0.013, 0.015, and 0.014 percent, respectively.

We use these latter numbers, even though the uncorrected numbers seem more consistent with the numbers obtained from IPUMS.

References

Historical Statistics of the United States, Colonial Times to 1970, Bicentennial Edition, part 1 (U.S. Department of Commerce, Bureau of the Census, 1975)

Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas, and Matthew Sobek (2018) IPUMS USA: Version 8.0 [dataset]. Minneapolis, MN: IPUMS. doi:10.18128/D010.V8.0

Matthew Sobek (2006) Chapter Ba. "Labor Occupations" in Susan B. Carter, ed., Historical Statistics of the United States, Cambridge U. Pr.

Ian D. Wyatt and Daniel E. Hecker (2006) "Occupational changes during the 20th century", Monthly Labor Review, March 2006, pp. 35-57

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Examples

data(AccountantsAuditorsPct)
plot(names(AccountantsAuditorsPct), AccountantsAuditorsPct, 
  type='l', log='y', cex.axis=1.8)
# for the version of this contributed to Wikimedia Commons

Description

Countries and codes used by the Armed Conflict Location and Event Data project with population and Gross Domestic Project (GDP) numbers for recent years. Population and GDP data are from the World Bank when available and from other sources otherwise. When no World Bank data are available, numbers may be reported from the closest year conveniently available, as noted in Comments; in those cases, the data may not be as accurate as the numbers from the World Bank.

NOTE: This code will be offered to the maintainer of the acled.api package. If they like it, it may not stay in Ecdat.

Usage

data(ACLEDpopGDP)

Format

A dataframe with rownames = ACLEDcountry containing :

ACLEDcountry

A character vector of the country names used by ACLED in the monthly totals of events and deaths between 2021-01 and 2024-09 extracted 2024-10-24.

ISO3

3-character ISO 3166-1 code for Country.

WBcountry

A character vector of the country names used by the World Bank (WB) in data extracted 2024-11-06.

pop2020, pop2021, pop2022, pop2023, pop, GDPpcn2020, GDPpcn2021, GDPpcn2022, GDPpcn2023, GDPpcn, GDPpcp2020, GDPpcp2021, GDPpcp2022, GDPpcp2023, GDPpcp

World Bank population and nominal Gross Domestic Product per capita (GDPpcn) in constant 2015 US$ plus GDP per capita, PPP (constant 2021 international $) extracted 2024-11-13 for the indicated years unless otherwise specified in "Comments". For country subdivions like Jersey, the World Bank extract used did not include such numbers. For those "countries", numbers were taken from Wikipedia and assigned to the nearest year in the 2020:2023 range and noted in "Comments".

Comments

Blank (”) if the data is from the World Bank. Otherwise, this lists the source of the population and GDP data, the applicable year, and other anomonlies.

Source

ACLED Explorer was used 2024-10-24 to download monthly totals between 2021-01 and 2024-09 of events and death in two files: one for events and another for deaths. Both had data on 234 "countries", though some were actually subdivisions. For example, ACLED "countries" includes the "Bailiwick of Jersey", which is a "British Crown" dependency, and the World Bank does not provide data on them as they do on souvereign countries.

However, the country names used by ACLED Explorer do not match the country names used by the World Bank.

This ACLEDpopGDP data.frame was created to facilitate merging ACLED data with data on population and GDP ... from the World Bank when avaialable and from other sources when not.

I got most of the ISO 3166-1 3-character country codes using findCountry. That function was NOT able to find country codes for the Caribbean Netherlands, Christmas Island, eSwatini, and North Macedonia, which have 3-letter ISO 3166-1 codes of BES, CXR, SWZ, and MKD, respectively.

From the World Bank website, I got something by clicking DataBank. From there, I clicked on "Population, total". This displayed numbers by country and year from 2008 to 2015. I clicked, "Add Time". From there I clicked "Unselect all" then selected 2020, 2021, 2022, and 2023. Then I clicked "x" in the upper right and "Apply Changes".

Then I clicked "Add Series". From there I found that many series I did not want were selected, so I clicked "Unselect all", then selected "GDP (constant 2015 US$)" and "Population, total". Then I clicked "x" in the upper right and "Apply Changes" as before.

Then I clicked "Download options" and selected "Excel". That downloaded a file named 'P_Popular Indicators.xlsx', which I moved to the working directory, read into R and merged in the obvious way to create most of ACLEDpopGDP.

For "Countries" not in the World Bank data I extracted, I got numbers from relevant Wikipedia articles and documented the source in ACLEDpopGDP[, "Comments"].

References

Armed Conflict Location and Event Data

DataBank

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Examples

# Country in World Bank data
ACLEDpopGDP['China', ]

# Country NOT in World Bank data
ACLEDpopGDP['Taiwan', ]

# Partial matching works if unique
ACLEDpopGDP['Czech',]

# Partial matching does NOT work if not unique
ACLEDpopGDP['Saint', ]
# Instead use, e.g., grep
ACLEDpopGDP[grep('Saint', ACLEDpopGDP[, 'ACLEDcountry']), ]

# If you know the ISO 3166-1 3-letter code:
ACLEDpopGDP['CPV'==ACLEDpopGDP[, 'ISO3'], ]
# NOTE: In this example, ACLEDcountry != 
# WBcountry.  

# No NAs in pop

all.equal(length(which(is.na(ACLEDpopGDP$pop))), 0)


# Only one NA in GDPpcn and GDPpcp: 
(GDPpNA <- which(is.na(ACLEDpopGDP$GDPpcp)))
(GDPnNA <- which(is.na(ACLEDpopGDP$GDPpcn)))
# Antarctica: 

all.equal(ACLEDpopGDP$ACLEDcountry[GDPpNA], 'Antarctica')



# Normal probability plots of population 
qqnorm(unlist(Pops), datax=TRUE)
qqnorm(unlist(Pops), datax=TRUE, log='x')

(billion <- which(unlist(Pops)>1e9))
# 2*5 = 10:
# Probably India and China
ACLEDpopGDP[c('China', 'India'), ]
ACLEDpopGDP[c('China', 'India'), pops] / 1e9

# Normal probability plot of GDPpc
GDPpc <- ACLEDpopGDP[c(GDPp, GDPn)]
qqnorm(unlist(GDPpc), datax=TRUE)
qqnorm(unlist(GDPpc), datax=TRUE, log='x')

Description

a panel of 6 observations from 1970 to 1984

number of observations : 90

observation : production units

country : United States

Usage

data(Airline)

Format

A dataframe containing :

airline

airline

year

year

cost

total cost, in $1,000

output

output, in revenue passenger miles, index number

pf

fuel price

lf

load factor, the average capacity utilization of the fleet

References

Greene, W.H. (2003) Econometric Analysis, Prentice Hall, https://archive.org/details/econometricanaly0000gree_f4x3, Table F7.1.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations, Index.Time.Series

Description

a cross-section from 1972

number of observations : 30

observation : regional

country : United States

Usage

data(Airq)

Format

A dataframe containing :

airq

indicator of air quality (the lower the better)

vala

value added of companies (in thousands of dollars)

rain

amount of rain (in inches)

coas

is it a coastal area ?

dens

population density (per square mile)

medi

average income per head (in US dollars)

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 4.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

A data.frame identifying which of 70 countries had a banking crisis each year 1800:2010. The first column is year. The remaining columns carry the names of the countries; those columns are 1 for years with banking crises and 0 otherwise.

Usage

data(bankingCrises)

Format

A data.frame

Details

This file was created using the following command:

bankingCrises <- readFinancialCrisisFiles(FinancialCrisisFiles)

HOWEVER: This function was in Ecfun 0.2-3 but was removed in 0.2-4. It used gdata::read.xls, and gdata users were informed that gdata might be removed from CRAN, and any package that used it would also be removed. It seemed that the database that this function was designed to read may not have been updated, which suggested that it made sense to remove this function, because it there may not be any further need for it.

This dataset is an update of a subset of the data used to create Figure 10.1. Capital Mobility and the Incidence of Banking Crises, All Countries, 1800-2008, Reinhart and Rogoff (2009, p. 156).

The general upward trend visible in a plot of these data may be attributed to at least two different factors:

(1) The gradual increase in the proportion of human labor that is monetized.

(2) An increase in the general ability of cronies of those in power to gamble with other people's money in forming and bankrupting financial institutions. The marked feature of this plot is the virtual absence of banking crises during the period of the Bretton Woods agreement, 1944 to 1971. This period ended when US President Nixon in effect canceled the Bretton Woods agreement by taking the US off the silver standard.

Author(s)

Spencer Graves

Source

http://www.reinhartandrogoff.com

References

Carmen M. Reinhart and Kenneth S. Rogoff (2009) This Time Is Different: Eight Centuries of Financial Folly, Princeton U. Pr.

Examples

data(bankingCrises)
numberOfCrises <- rowSums(bankingCrises[-1], na.rm=TRUE)
plot(bankingCrises$year, numberOfCrises, type='b')

# Write to a file for Wikimedia Commons
## Not run: 
if(FALSE){
  svg('bankingCrises.svg')
  plot(bankingCrises$year, numberOfCrises, type='b', 
    cex.axis=2, las=1, xlab='', ylab='', bty='n', cex=0.5)
  abline(v=c(1945, 1971), lty='dashed', col='blue')
  text(1958, 14, 'Bretton Woods', srt=90, cex=2, col='blue')
  dev.off()
  }
  
## End(Not run)

Description

a cross-section from 1972

number of observations : 4877

observation : individuals

country : United States

Usage

data(Benefits)

Format

A time series containing :

stateur

state unemployment rate (in %)

statemb

state maximum benefit level

state

state of residence code

age

age in years

tenure

years of tenure in job lost

joblost

a factor with levels (slack_work,position_abolished,seasonal_job_ended,other)

nwhite

non-white ?

school12

more than 12 years of school ?

sex

a factor with levels (male,female)

bluecol

blue collar worker ?

smsa

lives in SMSA ?

married

married ?

dkids

has kids ?

dykids

has young kids (0-5 yrs) ?

yrdispl

year of job displacement (1982=1,..., 1991=10)

rr

replacement rate

head

is head of household ?

ui

applied for (and received) UI benefits ?

Source

McCall, B.P. (1995) “The impact of unemployment insurance benefit levels on recipiency”, Journal of Business and Economic Statistics, 13, 189–198.

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 7.

Journal of Business Economics and Statistics web site : https://amstat.tandfonline.com/loi/ubes20.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

a cross-section

number of observations : 126

observation : production units

country : United States

Usage

data(Bids)

Format

A dataframe containing :

docno

doc no.

weeks

weeks

numbids

count

takeover

delta (1 if taken over)

bidprem

bid Premium

insthold

institutional holdings

size

size measured in billions

leglrest

legal restructuring

rearest

real restructuring

finrest

financial restructuring

regulatn

regulation

whtknght

white knight

Source

Jaggia, Sanjiv and Satish Thosar (1993) “Multiple Bids as a Consequence of Target Management Resistance”, Review of Quantitative Finance and Accounting, 447–457.

Cameron, A.C. and Per Johansson (1997) “Count Data Regression Models using Series Expansions: with Applications”, Journal of Applied Econometrics, 12, may, 203–223.

References

Cameron, A.C. and Trivedi P.K. (1998) Regression analysis of count data, Cambridge University Press, http://cameron.econ.ucdavis.edu/racd/racddata.html, chapter 5.

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

data.frame of cyber security breaches involving health care records of 500 or more humans reported to the U.S. Department of Health and Human Services (HHS) as of June 27, 2014.

Usage

data(breaches)

Format

A data.frame with 1055 observations on the following 24 variables:

Number

integer record number in the HHS data base

Name_of_Covered_Entity

factor giving the name of the entity experiencing the breach

State

Factor giving the 2-letter code of the state where the breach occurred. This has 52 levels for the 50 states plus the District of Columbia (DC) and Puerto Rico (PR).

Business_Associate_Involved

Factor giving the name of a subcontractor (or blank) associated with the breach.

Individuals_Affected

integer number of humans whose records were compromised in the breach. This is 500 or greater; U.S. law requires reports of breaches involving 500 or more records but not of breaches involving fewer.

Date_of_Breach

character vector giving the date or date range of the breach. Recodes as Dates in breach_start and breach_end.

Type_of_Breach

factor with 29 levels giving the type of breach (e.g., "Theft" vs. "Unauthorized Access/Disclosure", etc.)

Location_of_Breached_Information

factor with 41 levels coding the location from which the breach occurred (e.g., "Paper", "Laptop", etc.)

Date_Posted_or_Updated

Date the information was posted to the HHS data base or last updated.

Summary

character vector of a summary of the incident.

breach_start

Date of the start of the incident = first date given in Date_of_Breach above.

breach_end

Date of the end of the incident or NA if only one date is given in Date_of_Breach above.

year

integer giving the year of the breach

Details

The data primarily consists of breaches that occurred from 2010 through early 2014 when the extract was taken. However, a few breaches are recorded including 1 from 1997, 8 from 2002-2007, 13 from 2008 and 56 from 2009. The numbers of breaches from 2010 - 2014 are 211, 229, 227, 254 and 56, respectively. (A chi-square test for equality of the counts from 2010 through 2013 is 4.11, which with 3 degrees of freedom has a significance probability of 0.25. Thus, even though the lowest number is the first and the largest count is the last, the apparent trend is not statistically significant under the usual assumption of independent Poisson trials.)

The following corrections were made to the file:

Author(s)

Spencer Graves

Source

U.S. Department of Health and Human Services: Health Information Privacy: Breaches Affecting 500 or More Individuals

See Also

HHSCyberSecurityBreaches for a version of these data downloaded more recently. This newer version includes changes in reporting and in the variables included in the data.frame.

Examples

data(breaches)
quantile(breaches$Individuals_Affected)
# confirm that the smallest number is 500 
# -- and the largest is 4.9e6
# ... and there are no NAs

dDays <- with(breaches, breach_end - breach_start)
quantile(dDays, na.rm=TRUE)
# confirm that breach_end is NA or is later than 
# breach_start

Description

a cross-section from 1980

number of observations : 23972

observation : households

country : Spain

Usage

data(BudgetFood)

Format

A dataframe containing :

wfood

percentage of total expenditure which the household has spent on food

totexp

total expenditure of the household

age

age of reference person in the household

size

size of the household

town

size of the town where the household is placed categorized into 5 groups: 1 for small towns, 5 for big ones

sex

sex of reference person (man,woman)

Source

Delgado, A. and Juan Mora (1998) “Testing non–nested semiparametric models : an application to Engel curves specification”, Journal of Applied Econometrics, 13(2), 145–162.

References

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1973 to 1992

number of observations : 1729

observation : households

country : Italy

Usage

data(BudgetItaly)

Format

A dataframe containing :

wfood

food share

whouse

housing and fuels share

wmisc

miscellaneous share

pfood

food price

phouse

housing and fuels price

pmisc

miscellaneous price

totexp

total expenditure

year

year

income

income

size

household size

pct

cellule weight

Source

Bollino, Carlo Andrea, Frederico Perali and Nicola Rossi (2000) “Linear household technologies”, Journal of Applied Econometrics, 15(3), 253–274.

References

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1980 to 1982

number of observations : 1519

observation : households

country : United Kingdom

Usage

data(BudgetUK)

Format

A dataframe containing :

wfood

budget share for food expenditure

wfuel

budget share for fuel expenditure

wcloth

budget share for clothing expenditure

walc

budget share for alcohol expenditure

wtrans

budget share for transport expenditure

wother

budget share for other good expenditure

totexp

total household expenditure (rounded to the nearest 10 UK pounds sterling)

income

total net household income (rounded to the nearest 10 UK pounds sterling)

age

age of household head

children

number of children

Source

Blundell, Richard, Alan Duncan and Krishna Pendakur (1998) “Semiparametric estimation and consumer demand”, Journal of Applied Econometrics, 13(5), 435–462.

References

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1994

number of observations : 1472

observation : individuals

country : Belgium

Usage

data(Bwages)

Format

A dataframe containing :

wage

gross hourly wage rate in euro

educ

education level from 1 [low] to 5 [high]

exper

years of experience

sex

a factor with levels (males,female)

Source

European Community Household Panel.

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 3.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

monthly observations from 1960–01 to 2002–12

number of observations : 516

Usage

data(Capm)

Format

A time series containing :

rfood

excess returns food industry

rdur

excess returns durables industry

rcon

excess returns construction industry

rmrf

excess returns market portfolio

rf

risk-free return

Source

most of the above data are from Kenneth French's data library at http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 2.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

a cross-section

number of observations : 4654

observation : individuals

country : United States

Usage

data(Car)

Format

A dataframe containing :

choice

choice of a vehicle among 6 propositions

college

college education ?

hsg2

size of household greater than 2 ?

coml5

commute lower than 5 miles a day ?

typez

body type, one of regcar (regular car), sportuv (sport utility vehicle), sportcar, stwagon (station wagon), truck, van, for each proposition z from 1 to 6

fuelz

fuel for proposition z, one of gasoline, methanol, cng (compressed natural gas), electric.

pricez

price of vehicle divided by the logarithm of income

rangez

hundreds of miles vehicle can travel between refuelings/rechargings

accz

acceleration, tens of seconds required to reach 30 mph from stop

speedz

highest attainable speed in hundreds of mph

pollutionz

tailpipe emissions as fraction of those for new gas vehicle

sizez

0 for a mini, 1 for a subcompact, 2 for a compact and 3 for a mid–size or large vehicle

spacez

fraction of luggage space in comparable new gas vehicle

costz

cost per mile of travel (tens of cents) : home recharging for electric vehicle, station refueling otherwise

stationz

fraction of stations that can refuel/recharge vehicle

Source

McFadden, Daniel and Kenneth Train (2000) “Mixed MNL models for discrete response”, Journal of Applied Econometrics, 15(5), 447–470.

References

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1998-1999

number of observations : 420

observation : schools

country : United States

Usage

data(Caschool)

Format

A dataframe containing :

distcod

district code

county

county

district

district

grspan

grade span of district

enrltot

total enrollment

teachers

number of teachers

calwpct

percent qualifying for CalWORKS

mealpct

percent qualifying for reduced-price lunch

computer

number of computers

testscr

average test score (read.scr+math.scr)/2

compstu

computer per student

expnstu

expenditure per student

str

student teacher ratio

avginc

district average income

elpct

percent of English learners

readscr

average reading score

mathscr

average math score

Source

California Department of Education https://www.cde.ca.gov.

References

Stock, James H. and Mark W. Watson (2003) Introduction to Econometrics, Addison-Wesley Educational Publishers, chapter 4–7.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section

number of observations : 2798

observation : individuals

country : United States

Usage

data(Catsup)

Format

A dataframe containing :

id

individuals identifiers

choice

one of heinz41, heinz32, heinz28, hunts32

disp.z

is there a display for brand z ?

feat.z

is there a newspaper feature advertisement for brand z ?

price.z

price of brand z

Source

Jain, Dipak C., Naufel J. Vilcassim and Pradeep K. Chintagunta (1994) “A random–coefficients logit brand–choice model applied to panel data”, Journal of Business and Economics Statistics, 12(3), 317.

References

Journal of Business Economics and Statistics web site : https://amstat.tandfonline.com/loi/ubes20.

See Also

Ketchup, Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a panel of 46 observations from 1963 to 1992

number of observations : 1380

observation : regional

country : United States

Usage

data(Cigar)

Format

A dataframe containing :

state

state abbreviation

year

the year

price

price per pack of cigarettes

pop

population

pop16

population above the age of 16

cpi

consumer price index (1983=100)

ndi

per capita disposable income

sales

cigarette sales in packs per capita

pimin

minimum price in adjoining states per pack of cigarettes

Source

Baltagi, B.H. and D. Levin (1992) “Cigarette taxation: raising revenues and reducing consumption”, Structural Changes and Economic Dynamics, 3, 321–335.

Baltagi, B.H., J.M. Griffin and W. Xiong (2000) “To pool or not to pool: homogeneous versus heterogeneous estimators applied to cigarette demand”, Review of Economics and Statistics, 82, 117–126.

References

Baltagi, Badi H. (2003) Econometric analysis of panel data, John Wiley and sons, https://www.wiley.com/legacy/wileychi/baltagi/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

a panel of 48 observations from 1985 to 1995

number of observations : 528

observation : regional

country : United States

Usage

data(Cigarette)

Format

A dataframe containing :

state

state

year

year

cpi

consumer price index

pop

state population

packpc

number of packs per capita

income

state personal income (total, nominal)

tax

average state, federal, and average local excise taxes for fiscal year

avgprs

average price during fiscal year, including sales taxes

taxs

average excise taxes for fiscal year, including sales taxes

Source

Professor Jonathan Gruber, MIT.

References

Stock, James H. and Mark W. Watson (2003) Introduction to Econometrics, Addison-Wesley Educational Publishers, chapter 10.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

a cross-section from 1990

number of observations : 400

observation : production units

country : Netherland

Usage

data(Clothing)

Format

A dataframe containing :

tsales

annual sales in Dutch guilders

sales

sales per square meter

margin

gross-profit-margin

nown

number of owners (managers)

nfull

number of full-timers

npart

number of part-timers

naux

number of helpers (temporary workers)

hoursw

total number of hours worked

hourspw

number of hours worked per worker

inv1

investment in shop-premises

inv2

investment in automation.

ssize

sales floor space of the store (in m$^2$).

start

year start of business

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 3.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1993 to 1995

number of observations : 6259

observation : goods

country : United States

Usage

data(Computers)

Format

A dataframe containing :

price

price in US dollars of 486 PCs

speed

clock speed in MHz

hd

size of hard drive in MB

ram

size of Ram in in MB

screen

size of screen in inches

cd

is a CD-ROM present ?

multi

is a multimedia kit (speakers, sound card) included ?

premium

is the manufacturer was a "premium" firm (IBM, COMPAQ) ?

ads

number of 486 price listings for each month

trend

time trend indicating month starting from January of 1993 to November of 1995.

Source

Stengos, T. and E. Zacharias (2005) “Intertemporal pricing and price discrimination : a semiparametric hedonic analysis of the personal computer market”, Journal of Applied Econometrics, forthcoming.

References

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

quarterly observations from 1947-1 to 1996-4

number of observations : 200

observation : country

country : Canada

Usage

data(Consumption)

Format

A time series containing :

yd

personal disposable income, 1986 dollars

ce

personal consumption expenditure, 1986 dollars

References

Davidson, R. and James G. MacKinnon (2004) Econometric Theory and Methods, New York, Oxford University Press, chapter 1, 3, 4, 6, 9, 10, 14 and 15.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

Average surface temperature changes world wide and in the Northern Hemisphere 3 and 10 years after the injections of 5, 50 and 150 Tg (teragrams = millions of metric tons) of smoke into the upper troposphere, per Robock, Oman, and Stenchikov (2007).

These numbers are relative to the average for 1925-1975, which explains why the numbers are positive with smoke = 0.

Usage

data(coolingFromNuclearWar)

Format

A dataframe containing :

smoke

teragrams = millions of metric tons

dC3g, dC10g, dC3n, dC10n

average change in surface temperature 3 and 10 years after injection of smoke into the upper troposphere globally (g) or in the Northern Hemisphere (n) in degrees Celsius.

Source

Alan Robock, Luke Oman, and Georgiy L. Stenchikov (2007) Nuclear winter revisited with a modern climate model and current nuclear arsenals: Still catastrophic consequences, Journal of Geophysical Research, 112

Examples

data(coolingFromNuclearWar)
matplot(coolingFromNuclearWar[, 'smoke'], 
    coolingFromNuclearWar[, 2:5], type='l')
(linFit <- lm(cbind(dC3g, dC10g, dC3n, dC10n)~smoke, 
      coolingFromNuclearWar))
      
# total change 
dC <- as.matrix(coolingFromNuclearWar[, 2:5] - 
        rep(unlist(coolingFromNuclearWar[1, -1]), e=4))
(linFit0 <- lm(dC~smoke, coolingFromNuclearWar))
summary(linFit0)

Description

a cross-section from 1998

number of observations : 11130

observation : individuals

country : United States

Usage

data(CPSch3)

Format

A dataframe containing :

year

survey year

ahe

average hourly earnings

sex

a factor with levels (male,female)

Source

Bureau of labor statistics, U.S. Department of Labor https://www.bls.gov.

References

Stock, James H. and Mark W. Watson (2003) Introduction to Econometrics, Addison-Wesley Educational Publishers, chapter 3.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section

number of observations : 3292

observation : individuals

country : United States

Usage

data(Cracker)

Format

A dataframe containing :

id

individuals identifiers

choice

one of sunshine, kleebler, nabisco, private

disp.z

is there a display for brand z ?

feat.z

is there a newspaper feature advertisement for brand z ?

price.z

price of brand z

Source

Jain, Dipak C., Naufel J. Vilcassim and Pradeep K. Chintagunta (1994) “A random–coefficients logit brand–choice model applied to panel data”, Journal of Business and Economics Statistics, 12(3), 317.

Paap, R. and Philip Hans Frances (2000) “A dynamic multinomial probit model for brand choices with different short–run effects of marketing mix variables”, Journal of Applied Econometrics, 15(6), 717–744.

References

Journal of Business Economics and Statistics web site : https://amstat.tandfonline.com/loi/ubes20.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

Data casually collected on the number of packages on the Comprehensive R Archive Network (CRAN) at different dates.

NOTE: This could change in the future. See Details below.

Usage

data(CRANpackages)

Format

A data.frame containing:

Version

an ordered factor of the R version number primarily in use at the time. This was taken from archives of the major releases at https://svn.r-project.org/R/branches/R-1-3-patches/tests/internet.Rout.save, ... https://svn.r-project.org/R/branches/R-3-1-branch/tests/internet.Rout.save

Date

an object of class Date giving the date on which the count of the number of CRAN packages was determined.

Packages

an integer number of packages on the CRAN mirror checked on the indicated Date.

Source

A factor giving the source (person) who collected the data.

Details

This seems to provide the most widely available source for data on the growth of CRAN, manually recorded by John Fox and Spencer Graves. For a discussion of these and related data, see Fox (2009).

For more detail, see the CRAN packages data on GitHub maintained by Hadley Wickham. This contains the description file of every package uploaded to CRAN prior to the date of Hadley's most recent update. The current maintainer of the Ecdat and Ecfun packages would consider contributions along the following lines:

1. It might be nice to have a more complete dataset or datasets showing CRAN growth. This might include code fitting multiple models and predicting future growth with error bounds computed using Bayesian Model Averaging. These model fits might make an interesting addition to the examples in this help file. With a little more effort, it might make an interesting note for R Journal. Functions written to fit those models might be added to the Ecfun package.

2. It might be nice to have a function in Ecfun to download the CRAN packages data from GitHub and convert it to a format suitable for updating this dataset.

The current maintainer for Ecdat and Ecfun (Spencer Graves) might be willing to accept code and documentation for this but is not ready to do it himself at the present time.

Source

John Fox, "Aspects of the Social Organization and Trajectory of the R Project", R Journal, 1(2), Dec. 2009, 5-13. https://journal.r-project.org/archive/2009-2/RJournal_2009-2_Fox.pdf, accessed 2014-04-13.

Examples

plot(Packages~Date, CRANpackages, log='y')
# almost exponential growth

Description

a panel of 90 observations from 1981 to 1987

number of observations : 630

observation : regional

country : United States

Usage

data(Crime)

Format

A dataframe containing :

county

county identifier

year

year from 1981 to 1987

crmrte

crimes committed per person

prbarr

'probability' of arrest

prbconv

'probability' of conviction

prbpris

'probability' of prison sentence

avgsen

average sentence, days

polpc

police per capita

density

hundreds of people per square mile

taxpc

tax revenue per capita

region

one of 'other', 'west' or 'central'

smsa

'yes' or 'no' if in SMSA

pctmin

percentage minority in 1980

wcon

weekly wage in construction

wtuc

weekly wage in trns, util, commun

wtrd

weekly wage in whole sales and retail trade

wfir

weekly wage in finance, insurance and real estate

wser

weekly wage in service industry

wmfg

weekly wage in manufacturing

wfed

weekly wage of federal employees

wsta

weekly wage of state employees

wloc

weekly wage of local governments employees

mix

offense mix: face-to-face/other

pctymle

percentage of young males

Note

Thanks to Yungfong "Frank" Tang for identifying an error in the description of "density", previously documented erroneously as only "people per square mile".

Source

Cornwell, C. and W.N. Trumbull (1994) “Estimating the economic model of crime with panel data”, Review of Economics and Statistics, 76, 360–366.

Baltagi, B. H. (2006) “Estimating an economic model of crime using panel data from North Carolina”, Journal of Applied Econometrics, 21(4), May/June 2006, pp. 543-547.

See also: CRIME4.DES and Baltagi in JAE Data Archive.

References

Baltagi, Badi H. (2003) Econometric analysis of panel data, John Wiley and sons, https://www.wiley.com/legacy/wileychi/baltagi/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations, Index.Time.Series, Crime

Description

daily observations from 1969-1-03 to 1998-12-31

number of observations : 2528

observation : production units

country : United States

Usage

data(CRSPday)

Format

A dataframe containing :

year

the year

month

the month

day

the day

ge

the return for General Electric, PERMNO 12060

ibm

the return for IBM, PERMNO 12490

mobil

the return for Mobil Corporation, PERMNO 15966

crsp

the return for the CRSP value-weighted index, including dividends

Source

Center for Research in Security Prices, Graduate School of Business, University of Chicago, 725 South Wells - Suite 800, Chicago, Illinois 60607, https://www.crsp.org.

References

Davidson, R. and James G. MacKinnon (2004) Econometric Theory and Methods, New York, Oxford University Press, chapter 7, 9 and 15.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

monthly observations from 1969-1 to 1998-12

number of observations : 360

observation : production units

country : United States

Usage

data(CRSPmon)

Format

A time series containing :

ge

the return for General Electric, PERMNO 12060

ibm

the return for IBM, PERMNO 12490

mobil

the return for Mobil Corporation, PERMNO 15966

crsp

the return for the CRSP value-weighted index, including dividends

Source

Center for Research in Security Prices, Graduate School of Business, University of Chicago, 725 South Wells - Suite 800, Chicago, Illinois 60607, https://www.crsp.org.

References

Davidson, R. and James G. MacKinnon (2004) Econometric Theory and Methods, New York, Oxford University Press, chapter 13.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

a cross-section from 2000

number of observations : 308

observation : goods

country : Singapore

Usage

data(Diamond)

Format

A dataframe containing :

carat

weight of diamond stones in carat unit

colour

a factor with levels (D,E,F,G,H,I)

clarity

a factor with levels (IF,VVS1,VVS2,VS1,VS2)

certification

certification body, a factor with levels ( GIA, IGI, HRD)

price

price in Singapore $

Source

Chu, Singfat (2001) “Pricing the C's of Diamond Stones”, Journal of Statistics Education, 9(2).

References

Journal of Statistics Education's data archive : http://jse.amstat.org/jse_data_archive.htm.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

weekly observations from 1975 to 1989

number of observations : 778

observation : country

country : Germany

Usage

data(DM)

Format

A dataframe containing :

date

the date of the observation (19850104 is January, 4, 1985)

s

the ask price of the dollar in units of DM in the spot market on Friday of the current week

f

the ask price of the dollar in units of DM in the 30-day forward market on Friday of the current week

s30

the bid price of the dollar in units of DM in the spot market on the delivery date on a current forward contract

Source

Bekaert, G. and R. Hodrick (1993) “On biases in the measurement of foreign exchange risk premiums”, Journal of International Money and Finance, 12, 115-138.

References

Hayashi, F. (2000) Econometrics, Princeton University Press, http://fhayashi.fc2web.com/hayashi_econometrics.htm, chapter 6, 438-443.

See Also

Pound, Yen, Index.Source, Index.Economics, Index.Econometrics, Index.Observations, Index.Time.Series

Description

a cross-section from 1986

number of observations : 485

observation : individuals

country : United States

Usage

data(Doctor)

Format

A dataframe containing :

doctor

the number of doctor visits

children

the number of children in the household

access

is a measure of access to health care

health

a measure of health status (larger positive numbers are associated with poorer health)

Source

Gurmu, Shiferaw (1997) “Semiparametric estimation of hurdle regression models with an application to medicaid utilization”, Journal of Applied Econometrics, 12(3), 225-242.

References

Davidson, R. and James G. MacKinnon (2004) Econometric Theory and Methods, New York, Oxford University Press, chapter 11.

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

DoctorContacts, DoctorAUS, Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1977–1978

number of observations : 5190

observation : individuals

country : Australia

Usage

data(DoctorAUS)

Format

A dataframe containing :

sex

sex

age

age

income

annual income in tens of thousands of dollars

insurance

insurance contract (medlevy : medibanl levy, levyplus : private health insurance, freepoor : government insurance due to low income, freerepa : government insurance due to old age disability or veteran status

illness

number of illness in past 2 weeks

actdays

number of days of reduced activity in past 2 weeks due to illness or injury

hscore

general health score using Goldberg's method (from 0 to 12)

chcond

chronic condition (np : no problem, la : limiting activity, nla : not limiting activity)

doctorco

number of consultations with a doctor or specialist in the past 2 weeks

nondocco

number of consultations with non-doctor health professionals (chemist, optician, physiotherapist, social worker, district community nurse, chiropodist or chiropractor) in the past 2 weeks

hospadmi

number of admissions to a hospital, psychiatric hospital, nursing or convalescent home in the past 12 months (up to 5 or more admissions which is coded as 5)

hospdays

number of nights in a hospital, etc. during most recent admission: taken, where appropriate, as the mid-point of the intervals 1, 2, 3, 4, 5, 6, 7, 8-14, 15-30, 31-60, 61-79 with 80 or more admissions coded as 80. If no admission in past 12 months then equals zero.

medecine

total number of prescribed and nonprescribed medications used in past 2 days

prescrib

total number of prescribed medications used in past 2 days

nonpresc

total number of nonprescribed medications used in past 2 days

Source

Cameron, A.C. and P.K. Trivedi (1986) “Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests”, Journal of Applied Econometrics, 1, 29-54..

References

Cameron, A.C. and Trivedi P.K. (1998) Regression analysis of count data, Cambridge University Press, http://cameron.econ.ucdavis.edu/racd/racddata.html, chapter 3.

See Also

Doctor, DoctorContacts, Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1977–1978

number of observations : 20186

Usage

data(DoctorContacts)

Format

A time series containing :

mdu

number of outpatient visits to a medical doctor

lc

log(coinsrate+1) where coinsurance rate is 0 to 100

idp

individual deductible plan ?

lpi

log(annual participation incentive payment) or 0 if no payment

fmde

log(max(medical deductible expenditure)) if IDP=1 and MDE>1 or 0 otherwise

physlim

physical limitation ?

ndisease

number of chronic diseases

health

self–rate health (excellent,good,fair,poor)

linc

log of annual family income (in $)

lfam

log of family size

educdec

years of schooling of household head

age

exact age

sex

sex (male,female)

child

age less than 18 ?

black

is household head black ?

Source

Deb, P. and P.K. Trivedi (2002) “The Structure of Demand for Medical Care: Latent Class versus Two-Part Models”, Journal of Health Economics, 21, 601–625.

References

Cameron, A.C. and P.K. Trivedi (2005) Microeconometrics : methods and applications, Cambridge, pp. 553–556 and 565.

See Also

Doctor, MedExp, DoctorAUS, Index.Source, Index.Economics, Index.Econometrics, Index.Observations, Index.Time.Series

Description

a cross-section from 1988-1989

number of observations : 4266

observation : individuals

country : United States

Usage

data(Earnings)

Format

A dataframe containing :

age

age groups, a factor with levels (g1,g2,g3)

y

average annual earnings, in 1982 US dollars

Source

Mills, Jeffery A. and Sourushe Zandvakili (1997) “Statistical Inference via Bootstrapping for Measures of Inequality”, Journal of Applied Econometrics, 12(2), pp. 133-150.

References

Davidson, R. and James G. MacKinnon (2004) Econometric Theory and Methods, New York, Oxford University Press, chapter 5 and 7.

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1970 to 1970

number of observations : 158

observation : production units

country : United States

Usage

data(Electricity)

Format

A dataframe containing :

cost

total cost

q

total output

pl

wage rate

sl

cost share for labor

pk

capital price index

sk

cost share for capital

pf

fuel price

sf

cost share for fuel

Source

Christensen, L. and W. H. Greene (1976) “Economies of scale in U.S. electric power generation”, Journal of Political Economy, 84, 655-676.

References

Greene, W.H. (2003) Econometric Analysis, Prentice Hall, https://archive.org/details/econometricanaly0000gree_f4x3, chapter 4, 317-320.

Hayashi, F. (2000) Econometrics, Princeton University Press, https://archive.org/details/econometrics0000haya, chapter 1, 76-84.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section

number of observations : 601

observation : individuals

country : United States

Usage

data(Fair)

Format

A dataframe containing :

sex

a factor with levels (male,female)

age

age

ym

number of years married

child

children ? a factor

religious

how religious, from 1 (anti) to 5 (very)

education

education

occupation

occupation, from 1 to 7, according to Hollingshead's classification (reverse numbering)

rate

self rating of marriage, from 1 (very unhappy) to 5 (very happy)

nbaffairs

number of affairs in past year

Source

Fair, R. (1977) “A note on the computation of the tobit estimator”, Econometrica, 45, 1723-1727.

https://fairmodel.econ.yale.edu/rayfair/pdf/1978A200.PDF.

References

Greene, W.H. (2003) Econometric Analysis, Prentice Hall, https://archive.org/details/econometricanaly0000gree_f4x3, Table F22.2.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a panel of 48 observations from 1982 to 1988

number of observations : 336

observation : regional

country : United States

Usage

data(Fatality)

Format

A dataframe containing :

state

state ID code

year

year

mrall

traffic fatality rate (deaths per 10000)

beertax

tax on case of beer

mlda

minimum legal drinking age

jaild

mandatory jail sentence ?

comserd

mandatory community service ?

vmiles

average miles per driver

unrate

unemployment rate

perinc

per capita personal income

Source

Pr. Christopher J. Ruhm, Department of Economics, University of North Carolina.

References

Stock, James H. and Mark W. Watson (2003) Introduction to Econometrics, Addison-Wesley Educational Publishers, chapter 8.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

FinancialCrisisFiles is an object of class financialCrisisFiles created by the financialCrisisFiles function in Ecfun. It describes files containing data on financial crises downloadable from https://web.archive.org/web/20150419090824/http://www.reinhartandrogoff.com/data/browse-by-topic/topics/7.

NOTE: When this dataset was created it was downloaded from http://www.reinhartandrogoff.com/data/browse-by-topic/topics/7. However, it was "Not Found" in testing on 2020-02-09. Fortunately the data are still available on the Internet Archive.

Usage

data(FinancialCrisisFiles)

Details

Reinhart and Rogoff (http://www.reinhartandrogoff.com) provide numerous data sets analyzed in their book, "This Time Is Different: Eight Centuries of Financial Folly". Of interest here are data on financial crises of various types for 70 countries spanning the years 1800 - 2010, downloadable from http://www.reinhartandrogoff.com/data/browse-by-topic/topics/7/.

Version 0.2-3 of the Ecfun package included a function financialCrisisFiles that produced a list of class financialCrisisFiles describing four different Excel files in very similar formats with one sheet per Country and a few extra descriptor sheets. This data object FinancialCrisisFiles was produced by that function. That function required the gdata package, and users of that package were advised to terminate use of it, because it was scheduled to be removed from CRAN along with all packages that used it. Since Reinhart and Rogoff seemed not to be actively maintaining that dataset, there seemed little need to do the work required to make the Ecfun::financialCrisisFiles work without gdata, so it was removed from Ecfun version 2.0-4.

Value

FinancialCrisisFiles is a list with components carrying the names of files to be read. Each component is a list of optional arguments to pass to do.call(read.xls, ...) to read the sheet with name = name of that component. (This read.xls was part of the gdata package, which may no longer be available on CRAN.)

This corresponds to the files downloaded from http://www.reinhartandrogoff.com/data/browse-by-topic/topics/7/ in January 2013 (except for the fourth, which was not available there because of an error with the web site but instead was obtained directly from Prof. Reinhart).

Author(s)

Spencer Graves

Source

http://www.reinhartandrogoff.com

References

Carmen M. Reinhart and Kenneth S. Rogoff (2009) This Time Is Different: Eight Centuries of Financial Folly, Princeton U. Pr.

Description

a cross-section

number of observations : 1182

observation : individuals

country : United States

Usage

data(Fishing)

Format

A dataframe containing :

mode

recreation mode choice, on of : beach, pier, boat and charter

price

price for chosen alternative

catch

catch rate for chosen alternative

pbeach

price for beach mode

ppier

price for pier mode

pboat

price for private boat mode

pcharter

price for charter boat mode

cbeach

catch rate for beach mode

cpier

catch rate for pier mode

cboat

catch rate for private boat mode

ccharter

catch rate for charter boat mode

income

monthly income

Source

Herriges, J. A. and C. L. Kling (1999) “Nonlinear Income Effects in Random Utility Models”, Review of Economics and Statistics, 81, 62-72.

References

Cameron, A.C. and P.K. Trivedi (2005) Microeconometrics : methods and applications, Cambridge, pp. 463–466, 486 and 491–495.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

monthly observations from 1979–01 to 2001–12

number of observations : 276

Usage

data(Forward)

Format

A time series containing :

usdbp

exchange rate USD/British Pound Sterling

usdeuro

exchange rate US D/Euro

eurobp

exchange rate Euro/Pound

usdbp1

1 month forward rate USD/Pound

usdeuro1

1 month forward rate USD/Euro

eurobp1

1 month forward rate Euro/Pound

usdbp3

3 month forward rate USD/Pound

usdeuro3

month forward rate USD/Euro

eurobp3

month forward rate Euro/Pound

Source

Datastream

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 4.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

a cross-section from 2002–03

number of observations : 227

observation : individuals

country : United States

Usage

data(FriendFoe)

Format

A dataframe containing :

sex

contestant's sex

white

is contestant white ?

age

contestant's age in years

play

contestant's choice : a factor with levels "foe" and "friend". If both players play "friend", they share the trust box, if both play "foe", both players receive zero prize, if one of them play "foe" and the other one "friend", the "foe" player receive the entire trust box and the "friend" player nothing

round

round in which contestant is eliminated, a factor with levels ("1","2","3")

season

season show, a factor with levels ("1","2")

cash

the amount of cash in the trust box

sex1

partner's sex

white1

is partner white ?

age1

partner's age in years

play1

partner's choice : a factor with levels "foe" and "friend"

win

money won by contestant

win1

money won by partner

Source

Kalist, David E. (2004) “Data from the Television Game Show "Friend or Foe?"”, Journal of Statistics Education, 12(3).

References

Journal of Statistics Education's data archive : http://jse.amstat.org/jse_data_archive.htm.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

daily observations from 1980–01 to 1987–05–21

number of observations : 1867

observation : country

country : World

Usage

data(Garch)

Format

A dataframe containing :

date

date of observation (yymmdd)

day

day of the week (a factor)

dm

exchange rate Dollar/Deutsch Mark

ddm

dm-dm(-1)

bp

exchange rate of Dollar/British Pound

cd

exchange rate of Dollar/Canadian Dollar

dy

exchange rate of Dollar/Yen

sf

exchange rate of Dollar/Swiss Franc

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 8.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

a panel of 18 observations from 1960 to 1978

number of observations : 342

observation : country

country : OECD

Usage

data(Gasoline)

Format

A dataframe containing :

country

a factor with 18 levels

year

the year

lgaspcar

logarithm of motor gasoline consumption per auto

lincomep

logarithm of real per-capita income

lrpmg

logarithm of real motor gasoline price

lcarpcap

logarithm of the stock of cars per capita

Source

Baltagi, B.H. and Y.J. Griggin (1983) “Gasoline demand in the OECD: an application of pooling and testing procedures”, European Economic Review, 22.

References

Baltagi, Badi H. (2003) Econometric analysis of panel data, John Wiley and sons, https://www.wiley.com/legacy/wileychi/baltagi/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

a cross-section from 1980

number of observations : 758

observation : individuals

country : United States

Usage

data(Griliches)

Format

A dataframe containing :

rns

residency in the southern states (first observation) ?

rns80

same variable for 1980

mrt

married (first observation) ?

mrt80

same variable for 1980

smsa

residency in metropolitan areas (first observation) ?

smsa80

same variable for 1980

med

mother's education in years

iq

IQ score

kww

score on the “knowledge of the world of work” test

year

year of the observation

age

age (first observation)

age80

same variable for 1980

school

completed years of schooling (first observation)

school80

same variable for 1980

expr

experience in years (first observation)

expr80

same variable for 1980

tenure

tenure in years (first observation)

tenure80

same variable for 1980

lw

log wage (first observation)

lw80

same variable for 1980

Source

Blackburn, M. and Neumark D. (1992) “Unobserved ability, efficiency wages, and interindustry wage differentials”, Quarterly Journal of Economics, 107, 1421-1436.

References

Hayashi, F. (2000) Econometrics, Princeton University Press, http://fhayashi.fc2web.com/hayashi_econometrics.htm, chapter 3, 250-256.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a panel of 20 annual observations from 1935 to 1954 on each of 10 firms.

number of observations : 200

observation : production units

country : United States

Usage

data(Grunfeld)

Format

A dataframe containing :

firm

observation

year

date

inv

gross Investment

value

value of the firm

capital

stock of plant and equipment

Details

There are several versions of these data.

GrunfeldGreene is "A data frame containing 20 annual observations on 3 variables for 5 firms." That dataset reportedly contains errors but is maintained in that way to avoid breaking the code of others who use it. That help file also provides a link to the corrected version.

See also for a version with only 5 firms.

Source

Moody's Industrial Manual, Survey of Current Business.

References

Greene, W.H. (2003) Econometric Analysis, Prentice Hall, Table F13.1.

Baltagi, Badi H. (2003) Econometric analysis of panel data, John Wiley and sons, https://www.wiley.com/legacy/wileychi/baltagi/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations, GrunfeldGreene,

Index.Time.Series

Description

a cross-section

number of observations : 250

observation : households

country : California

Usage

data(HC)

Format

A dataframe containing :

depvar

heating system, one of gcc (gas central heat with cooling), ecc (electric central resistance heat with cooling), erc (electric room resistance heat with cooling), hpc (electric heat pump which provides cooling also), gc (gas central heat without cooling, ec (electric central resistance heat without cooling), er (electric room resistance heat without cooling)

ich.z

installation cost of the heating portion of the system

icca

installation cost for cooling

och.z

operating cost for the heating portion of the system

occa

operating cost for cooling

income

annual income of the household

References

Kenneth Train's home page : https://eml.berkeley.edu/~train/.

See Also

Heating, Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section

number of observations : 900

observation : households

country : California

Usage

data(Heating)

Format

A dataframe containing :

idcase

id

depvar

heating system, one of gc (gas central), gr (gas room), ec (electric central), er (electric room), hp (heat pump)

ic.z

installation cost for heating system z (defined for the 5 heating systems)

oc.z

annual operating cost for heating system z (defined for the 5 heating systems)

pb.z

ratio oc.z/ic.z

income

annual income of the household

agehed

age of the household head

rooms

numbers of rooms in the house

References

Kenneth Train's home page : https://eml.berkeley.edu/~train/.

See Also

HC, Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section

number of observations : 506

observation : regional

country : United States

Usage

data(Hedonic)

Format

A dataframe containing :

mv

median value of owner–occupied homes

crim

crime rate

zn

proportion of 25,000 square feet residential lots

indus

proportion of nonretail business acres

chas

is the tract bounds the Charles River ?

nox

annual average nitrogen oxide concentration in parts per hundred million

rm

average number of rooms

age

proportion of owner units built prior to 1940

dis

weighted distances to five employment centers in the Boston area

rad

index of accessibility to radial highways

tax

full value property tax rate ($ / $10,000)

ptratio

pupil/teacher ratio

blacks

proportion of blacks in the population

lstat

proportion of population that is lower status

townid

town identifier

Source

Harrison, D. and D.L. Rubinfeld (1978) “Hedonic housing prices and the demand for clean air”, Journal of Environmental Economics Ans Management, 5, 81–102.

Belsley, D.A., E. Kuh and R. E. Welsch (1980) Regression diagnostics: identifying influential data and sources of collinearity, John Wiley, New–York.

References

Baltagi, Badi H. (2003) Econometric analysis of panel data, John Wiley and sons, https://www.wiley.com/legacy/wileychi/baltagi/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

Since October 2009 organizations in the U.S. that store data on human health are required to report any incident that compromises the confidentiality of 500 or more patients / human subjects (45 C.F.R. 164.408) These reports are publicly available. HHSCyberSecurityBreaches was downloaded from the Office for Civil Rights of the U.S. Department of Health and Human Services, 2015-02-26

Usage

data(HHSCyberSecurityBreaches)

Format

A dataframe containing 1151 observations of 9 variables:

Name.of.Covered.Entity

A character vector identifying the organization involved in the breach.

State

A factor giving the two-letter abbreviation of the US state or territory where the breach occurred. This has 52 levels for the 50 states plus the District of Columbia (DC) and Puerto Rico (PR).

Covered.Entity.Type

A factor giving the organization type of the covered entity with levels "Business Associate", "Health Plan", "Healthcare Clearing House", and "Healthcare Provider"

Individuals.Affected

An integer giving the number of humans whose records were compromised in the breach. This is 500 or greater; U.S. law requires reports of breaches involving 500 or more records but not of breaches involving fewer.

Breach.Submission.Date

Date when the breach was reported.

Type.of.Breach

A factor giving one of 29 different combinations of 7 different breach types, separated by ", ": "Hacking/IT Incident", "Improper Disposal", "Loss", "Other", "Theft", "Unauthorized Access/Disclosure", and "Unknown"

Location.of.Breached.Information

A factor giving one of 47 different combinations of 8 different location categories: "Desktop Computer", "Electronic Medical Record", "Email", "Laptop", "Network Server", "Other", "Other Portable Electronic Device", "Paper/Films"

Business.Associate.Present

Logical = (Covered.Entity.Type == "Business Associate")

Web.Description

A character vector giving a narrative description of the incident.

Details

This contains the breach report data downloaded 2015-02-26 from the US Health and Human Services. This catalogs reports starting 2009-10-21. Earlier downloads included a few breaches prior to 2009 when the law was enacted (inconsistently reported), and a date for breach occurrence in addition to the date of the report.

The following corrections were made to the file:

• UCLA Health System, breach date 11/4/2011, had cover entity added as "Healthcare Provider"

• Wyoming Department of Health, breach date 3/2/2010 had breach type changed to "Unauthorized Access / Disclosure"

• Computer Program and Systems, Inc. (CPSI), breach date 3/30/2010 had breach type changed to "Unauthorized Access / Disclosure"

• Aetna, breach date 7/27/2010 had breach type changed to "Improper Disposal' (see explanation below), breach date 5/24/2010 name changed to City of Charlotte, NC (Health Plan) and state changed to NC

• Mercer, breach date 7/30/2010 state changed to MI

• Not applicable, breach date 11/2/2011 name changed to Northridge Hospital Medical Center and state changed to CA

• na, breach date 4/4/2011 name changed to Brian J Daniels DDS, Paul R Daniels DDS, and state changed to AZ

• NA, breach date 5/27/2011 name changed to and Spartanburg Regional Healthcare System state changed to SC

• NA, breach date 7/4/2011 name changed to Yanz Dental Corporation and state changed to CA

Source

"Breaches Affecting 500 or More Individuals" downloaded from the Office for Civil Rights of the U.S. Department of Health and Human Services, 2015-02-26

See Also

breaches for an earlier download of these data. The exact reporting requirements and even the number and definitions of variables included in the data.frame have changed.

Examples

##
## 1.  mean(Individuals.Affected)
##
mean(HHSCyberSecurityBreaches$Individuals.Affected)
##
## 2.  Basic Breach Types
##
tb <- as.character(HHSCyberSecurityBreaches$Type.of.Breach)
tb. <- strsplit(tb, ', ')
table(unlist(tb.))
# 8 levels, but two are the same apart from 
# a trailing blank.  
##
## 3.  Location.of.Breached.Information 
##
lb <- as.character(HHSCyberSecurityBreaches[[
          'Location.of.Breached.Information']])
table(lb)
lb. <- strsplit(lb, ', ')
table(unlist(lb.))
# 8 levels 
table(sapply(lb., length))
#   1    2    3    4    5    6    7    8 
#1007  119   13    8    1    1    1    1 
# all 8 levels together observed once 
# There are 256 = 2^8 possible combinations 
# of which 47 actually occur in these data.

Description

a cross-section from 1993

number of observations : 22272

observation : individuals

country : United States

Usage

data(HI)

Format

A dataframe containing :

whrswk

hours worked per week by wife

hhi

wife covered by husband's HI ?

whi

wife has HI thru her job ?

hhi2

husband has HI thru own job ?

education

a factor with levels, "<9years", "9-11years", "12years", "13-15years", "16years", ">16years"

race

one of white, black, other

hispanic

Hispanic ?

experience

years of potential work experience

kidslt6

number of kids under age of 6

kids618

number of kids 6–18 years old

husby

husband's income in thousands of dollars

region

one of other, northcentral, south, west

wght

sampling weight

Source

Olson, Craig A. (1998) “A comparison of parametric and semiparametric estimates of the effect of spousal health insurance coverage on weekly hours worked by wives”, Journal of Applied Econometrics, 13(5), September–October, 543–565.

References

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1997-1998

number of observations : 2381 observation : individuals country : United States

In package version 0.2-9 and earlier this dataset was called Hdma.

Usage

data(Hmda)

Format

A dataframe containing :

dir

debt payments to total income ratio

hir

housing expenses to income ratio

lvr

ratio of size of loan to assessed value of property

ccs

consumer credit score from 1 to 6 (a low value being a good score)

mcs

mortgage credit score from 1 to 4 (a low value being a good score)

pbcr

public bad credit record ?

dmi

denied mortgage insurance ?

self

self employed ?

single

is the applicant single ?

uria

1989 Massachusetts unemployment rate in the applicant's industry

condominium

is unit a condominium ? (was called comdominiom in version 0.2-9 and earlier versions of the package)

black

is the applicant black ?

deny

mortgage application denied ?

Source

Federal Reserve Bank of Boston.

Munnell, Alicia H., Geoffrey M.B. Tootell, Lynne E. Browne and James McEneaney (1996) “Mortgage lending in Boston: Interpreting HMDA data”, American Economic Review, 25-53.

References

Stock, James H. and Mark W. Watson (2003) Introduction to Econometrics, Addison-Wesley Educational Publishers, chapter 9.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section from 1987

number of observations : 546

observation : goods

country : Canada

Usage

data(Housing)

Format

A dataframe containing :

price

sale price of a house

lotsize

the lot size of a property in square feet

bedrooms

number of bedrooms

bathrms

number of full bathrooms

stories

number of stories excluding basement

driveway

does the house has a driveway ?

recroom

does the house has a recreational room ?

fullbase

does the house has a full finished basement ?

gashw

does the house uses gas for hot water heating ?

airco

does the house has central air conditioning ?

garagepl

number of garage places

prefarea

is the house located in the preferred neighbourhood of the city ?

Source

Anglin, P.M. and R. Gencay (1996) “Semiparametric estimation of a hedonic price function”, Journal of Applied Econometrics, 11(6), 633-648.

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 3.

Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

quarterly observations from 1960-1 to 2001-4

number of observations : 168

observation : country

country : Canada

Usage

data(Hstarts)

Format

A time series containing :

hs

the log of urban housing starts in Canada, not seasonally adjusted, CANSIM series J6001, converted to quarterly

hssa

the log of urban housing starts in Canada, seasonally adjusted, CANSIM series J9001, converted to quarterly. Observations prior to 1966:1 are missing

References

Davidson, R. and James G. MacKinnon (2004) Econometric Theory and Methods, New York, Oxford University Press, chapter 13.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

four–weekly observations from 1951–03–18 to 1953–07–11

number of observations : 30

observation : country

country : United States

Usage

data(Icecream)

Format

A time series containing :

cons

consumption of ice cream per head (in pints);

income

average family income per week (in US Dollars);

price

price of ice cream (per pint);

temp

average temperature (in Fahrenheit);

Source

Hildreth, C. and J. Lu (1960) Demand relations with autocorrelated disturbances, Technical Bulletin No 2765, Michigan State University.

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 4.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

Data on quantiles of the distributions of family incomes in the United States. This combines three data sources:

(1) US Census Table F-1 for the central quantiles

(2) Piketty and Saez for the 95th and higher quantiles

(3) Gross Domestic Product and implicit price deflators from Measuring Worth. (NOTE: The Measuring Worth Web site, https://MeasuringWorth.com, often gives security warnings. The desired data still seems to be available and not corrupted, however.)

Usage

data(incomeInequality)

Format

A data.frame containing:

Year

numeric year 1947:2012

Number.thousands

number of families in the US

quintile1, quintile2, median, quintile3, quintile4, p95

quintile1, quintile2, quintile3, quintile4, and p95 are the indicated quantiles of the distribution of family income from US Census Table F-1. The media is computed as the geometric mean of quintile2 and quintile3. This is accurate to the extent that the lognormal distribution adequately approximates the central 20 percent of the income distribution, which it should for most practical purposes.

P90, P95, P99, P99.5, P99.9, P99.99

The indicated quantiles of family income per Piketty and Saez

realGDP.M, GDP.Deflator, PopulationK, realGDPperCap

real GDP in millions, GDP implicit price deflators, US population in thousands, and real GDP per capita, according to MeasuringWorth.com. (NOTE: The web address for this, https://MeasuringWorth.com, seems to be functional but may not be maintained to current internet security standards. It is therefore given here as text rather than a hot link.)

P95IRSvsCensus

ratio of the estimates of the 95th percentile of distributions of family income from the Piketty and Saez analysis of data from the Internal Revenue Service (IRS) and from the US Census Bureau.

The IRS has ranged between 72 and 98 percent of the Census Bureau figures for the 95th percentile of the distribution, with this ratio averaging around 75 percent since the late 1980s. However, this systematic bias is modest relative to the differences between the different quantiles of interest in this combined dataset.

personsPerFamily

average number of persons per family using the number of families from US Census Table F-1 and the population from MeasuringWorth. (Note: The web site for Measuring Worth, https://MeasuringWorth.com, often gives security warnings. It still seems to work. It seems that the web site is not maintained to current internet security standards.)

realGDPperFamily

personsPerFamily * realGDPperCap

mean.median

ratio of realGDPperFamily to the median. This is a measure of skewness and income inequality.

Details

For details on how this data.frame was created, see "F1.PikettySaez.R" in system.file('scripts', package='fda'). This provides links for files to download and R commands to read those files and convert them into an updated version of incomeInequality. This is a reasonable thing to do if it is more than 2 years since max(incomeInequality$year). All data are in constant 2012 dollars.

Author(s)

Spencer Graves

Source

United States Census Bureau, Table F-1. Income Limits for Each Fifth and Top 5 Percent of Families, All Races, https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-inequality.html, accessed 2016-12-09.

Thomas Piketty and Emmanuel Saez (2003) "Income Inequality in the United States, 1913-1998", Quarterly Journal of Economics, 118(1) 1-39, https://eml.berkeley.edu/~saez/, update accessed February 28, 2014.

Louis Johnston and Samuel H. Williamson (2011) "What Was the U.S. GDP Then?" MeasuringWorth. (Note: Their web address, https://www.measuringworth.org/usgdp, often gives security warnings. The desired data still seems to be available there. However, it seems that the site is not maintained to current internet security standards. The data used in the current USGDPpresidents data set was extracted February 28, 2014.)

Examples

##
## Rato of IRS to census estimates for the 95th percentile
##
data(incomeInequality)
plot(P95IRSvsCensus~Year, incomeInequality, type='b')
# starts ~0.74, trends rapidly up to ~0.97,
# then drifts back to ~0.75
abline(h=0.75)
abline(v=1989)
# check
sum(is.na(incomeInequality$P95IRSvsCensus))
# The Census data runs to 2011;  Pikety and Saez runs to 2010.
quantile(incomeInequality$P95IRSvsCensus, na.rm=TRUE)
# 0.72 ... 0.98

##
## Persons per Family
##

plot(personsPerFamily~Year, incomeInequality, type='b')
quantile(incomeInequality$personsPerFamily)
# ranges from 3.72 to 4.01 with median 3.84
#  -- almost 4

##
## GDP per family
##
plot(realGDPperFamily~Year, incomeInequality, type='b', log='y')

##
## Plot the mean then the first quintile, then the median,
##            99th, 99.9th and 99.99th percentiles
##
plotCols <- c(21, 3, 5, 11, 13:14)
kcols <- length(plotCols)
plotColors <- c(1:6, 8:13)[1:kcols] # omit 7=yellow
plotLty <- 1:kcols

matplot(incomeInequality$Year, incomeInequality[plotCols]/1000,
        log='y', type='l', col=plotColors, lty=plotLty)

#*** Growth broadly shared 1947 - 1970, then began diverging
#*** The divergence has been most pronounced among the top 1%
#*** and especially the top 0.01%

##
## Growth rate by quantile 1947-1970 and 1970 - present
##
keyYears <- c(1947, 1970, 2010)
(iYears <- which(is.element(incomeInequality$Year, keyYears)))

(dYears <- diff(keyYears))
kk <- length(keyYears)
(lblYrs <- paste(keyYears[-kk], keyYears[-1], sep='-'))

(growth <- sapply(incomeInequality[iYears,], function(x, labels=lblYrs){
    dxi <- exp(diff(log(x)))
    names(dxi) <- labels
    dxi
} ))

# as percent
(gr <- round(100*(growth-1), 1))

# The average annual income (realGDPperFamily) doubled between
# 1970 and 2010 (increased by 101 percent), while the median household
# income increased only 23 percent.

##
## Income lost by each quantile 1970-2010
## relative to the broadly shared growth 1947-1970
##
(lostGrowth <- (growth[, 'realGDPperFamily']-growth[, plotCols]))
# 1947-1970:  The median gained 20% relative to the mean,
#           while the top 1% lost ground
# 1970-2010:  The median lost 79%, the 99th percentile lost 29%,
#           while the top 0.1% gained

(lostIncome <- (lostGrowth[2, ] *
                incomeInequality[iYears[2], plotCols]))
# The median family lost $39,000 per year in income
# relative to what they would have with the same economic growth
# broadly shared as during 1947-1970.
# That's slightly over $36,500 per year = $100 per day

(grYr <- growth^(1/dYears))
(grYr. <- round(100*(grYr-1), 1))

##
## Regression line:  linear spline
##

(varyg <- c(3:14, 21))
Varyg <- names(incomeInequality)[varyg]
str(F01ps <- reshape(incomeInequality[c(1, varyg)], idvar='Year',
                     ids=F1.PikettySeaz$Year,
                     times=Varyg, timevar='pctile',
                     varying=list(Varyg), direction='long'))
names(F01ps)[2:3] <- c('variable', 'value')
F01ps$variable <- factor(F01ps$variable)

# linear spline basis function with knot at 1970
F01ps$t1970p <- pmax(0, F01ps$Year-1970)

table(nas <- is.na(F01ps$value))
# 6 NAs, one each of the Piketty-Saez variables in 2011
F01i <- F01ps[!nas, ]

# formula:
# log(value/1000) ~ b*Year + (for each variable:
#     different intercept + (different slope after 1970))

Fit <- lm(log(value/1000)~Year+variable*t1970p, F01i)
anova(Fit)
# all highly significant
# The residuals may show problems with the model,
# but we will ignore those for now.

# Model predictions
str(Pred <- predict(Fit))

##
## Combined plot
##
#  Plot to a file?  Wikimedia Commons prefers svg format.
## Not run: 
if(FALSE){
  svg('incomeInequality8.svg')
#  If you want software to convert svg to another format 
#  such as png, consider GIMP (www.gimp.org).

#  Base plot

# Leave extra space on the right to label 
# with growth since 1970
  op <- par(mar=c(5, 4, 4, 5)+0.1)

  matplot(incomeInequality$Year, 
      incomeInequality[plotCols]/1000,
      log='y', type='l', col=plotColors, lty=plotLty,
      xlab='', ylab='', las=1, axes=FALSE, lwd=3)
  axis(1, at=seq(1950, 2010, 10),
     labels=c(1950, NA, 1970, NA, 1990, NA, 2010), 
     cex.axis=1.5)
  yat <- c(10, 50, 100, 500, 1000, 5000, 10000)
  axis(2, yat, labels=c('$10K', '$50K', '$100K', '$500K',
             '$1M', '$5M', '$10M'), las=1, cex.axis=1.2)

#  Label the lines
  pctls <- paste(c(20, 40, 50, 60, 80, 90, 95, 99, 
      99.5, 99.9, 99.99),
              '%', sep='')
  lineLbl0 <- c('Year', 'families K', pctls,
     'realGDP.M', 'GDP deflator', 'pop-K', 'realGDPperFamily',
     '95 pct(IRS / Census)', 'size of household',
     'average family income', 'mean/median')
  (lineLbls <- lineLbl0[plotCols])
  sel75 <- (incomeInequality$Year==1975)

  laby <- incomeInequality[sel75, plotCols]/1000

  text(1973.5, c(1.2, 1.2, 1.3, 1.5, 1.9)*laby[-1], 
    lineLbls[-1], cex=1.2)
  text(1973.5, 1.2*laby[1], lineLbls[1], cex=1.2, srt=10)

##
## Add lines + points for the knots in 1970
##
  End <- numeric(kcols)
  F01names <- names(incomeInequality)
  for(i in seq(length=kcols)){
    seli <- (as.character(F01i$variable) == 
        F01names[plotCols[i]])
#  with(F01i[seli, ], lines(Year, exp(Pred[seli]), 
#  col=plotColors[i]))
    yri <- F01i$Year[seli]
    predi <- exp(Pred[seli])
    lines(yri, predi, col=plotColors[i])
    End[i] <- predi[length(predi)]
    sel70i <- (yri==1970)
    points(yri[sel70i], predi[sel70i], 
        col=plotColors[i])
  }

##
##  label growth rates
##
  table(sel70. <- (incomeInequality$Year>1969))
  (lastYrs <- incomeInequality[sel70., 'Year'])
  (lastYr. <- max(lastYrs)+4)
#text(lastYr., End, gR., xpd=NA)
  text(lastYr., End, paste(gr[2, plotCols], '%', sep=''), 
    xpd=NA)
  text(lastYr.+7, End, paste(grYr.[2, plotCols], '%', 
    sep=''), xpd=NA)

##
##  Label the presidents
##
  abline(v=c(1953, 1961, 1969, 1977, 1981, 1989, 1993, 
    2001, 2009))
  (m99.95 <- with(incomeInequality, sqrt(P99.9*P99.99))/1000)

  text(1949, 5000, 'Truman')
  text(1956.8, 5000, 'Eisenhower', srt=90)
  text(1963, 5000, 'Kennedy', srt=90)
  text(1966.8, 5000, 'Johnson', srt=90)
  text(1971, 5*m99.95[24], 'Nixon', srt=90)
  text(1975, 5*m99.95[28], 'Ford', srt=90)
  text(1978.5, 5*m99.95[32], 'Carter', srt=90)
  text(1985.1, m99.95[38], 'Reagan' )
  text(1991, 0.94*m99.95[44], 'GHW Bush', srt=90)
  text(1997, m99.95[50], 'Clinton')
  text(2005, 1.1*m99.95[58], 'GW Bush', srt=90)
  text(2010, 1.2*m99.95[62], 'Obama', srt=90)
##
##  Done
##
  par(op) # reset margins

  dev.off() # for plot to a file
  }
  
## End(Not run)

Description

quarterly observations from 1971–1 to 1985–2

number of observations : 58

observation : country

country : United Kingdom

Usage

data(IncomeUK)

Format

A time series containing :

income

total disposable income (million Pounds, current prices)

consumption

consumer expenditure (million Pounds, current prices)

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapters 8 and 9.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

• binomial model

  • Benefits : Unemployment of Blue Collar Workers

  • Hmda : The Boston HMDA Data Set

  • Mroz : Labor Supply Data

  • Participation : Labor Force Participation

  • Train : Stated Preferences for Train Traveling

• censored and truncated model

  • Fair : Extramarital Affairs Data

  • HI : Health Insurance and Hours Worked By Wives

  • Mofa : International Expansion of U.S. MOFAs (majority–owned Foreign Affiliates in Fire (finance, Insurance and Real Estate)

  • Tobacco : Households Tobacco Budget Share

  • Workinghours : Wife Working Hours

• count data

  • Accident : Ship Accidents

  • Bids : Bids Received By U.S. Firms

  • Doctor : Number of Doctor Visits

  • DoctorAUS : Doctor Visits in Australia

  • DoctorContacts : Contacts With Medical Doctor

  • OFP : Visits to Physician Office

  • PatentsHGH : Dynamic Relation Between Patents and R&D

  • PatentsRD : Patents, R&D and Technological Spillovers for a Panel of Firms

  • Somerville : Visits to Lake Somerville

  • StrikeNb : Number of Strikes in US Manufacturing

• duration model

  • Oil : Oil Investment

  • Strike : Strike Duration Data

  • StrikeDur : Strikes Duration

  • UnempDur : Unemployment Duration

  • Unemployment : Unemployment Duration

• multinomial model

  • Car : Stated Preferences for Car Choice

  • Catsup : Choice of Brand for Catsup

  • Cracker : Choice of Brand for Crackers

  • Fishing : Choice of Fishing Mode

  • HC : Heating and Cooling System Choice in Newly Built Houses in California

  • Heating : Heating System Choice in California Houses

  • Ketchup : Choice of Brand for Ketchup

  • Mode : Mode Choice

  • ModeChoice : Data to Study Travel Mode Choice

  • Tuna : Choice of Brand for Tuna

  • Yogurt : Choice of Brand for Yogurts

• ordered model

  • Kakadu : Willingness to Pay for the Preservation of the Kakadu National Park

  • Mathlevel : Level of Calculus Attained for Students Taking Advanced Micro–economics

  • NaturalPark : Willingness to Pay for the Preservation of the Alentejo Natural Park

• panel

  • Airline : Cost for U.S. Airlines

  • Cigar : Cigarette Consumption

  • Cigarette : The Cigarette Consumption Panel Data Set

  • Crime : Crime in North Carolina

  • Fatality : Drunk Driving Laws and Traffic Deaths

  • Gasoline : Gasoline Consumption

  • Grunfeld : Grunfeld Investment Data

  • LaborSupply : Wages and Hours Worked

  • Males : Wages and Education of Young Males

  • MunExp : Municipal Expenditure Data

  • Produc : Us States Production

  • SumHes : The Penn Table

  • Wages : Panel Data of Individual Wages

• system of equations

  • BudgetItaly : Budget Shares for Italian Households

  • BudgetUK : Budget Shares of British Households

  • Electricity : Cost Function for Electricity Producers

  • Klein : Klein's Model I

  • ManufCost : Manufacturing Costs

  • Nerlove : Cost Function for Electricity Producers, 1955

  • University : Provision of University Teaching and Research

• time–series

  • CRSPday : Daily Returns from the CRSP Database

  • CRSPmon : Monthly Returns from the CRSP Database

  • Capm : Stock Market Data

  • Consumption : Quarterly Data on Consumption and Expenditure

  • DM : DM Dollar Exchange Rate

  • Forward : Exchange Rates of US Dollar Against Other Currencies

  • Garch : Daily Observations on Exchange Rates of the US Dollar Against Other Currencies

  • Hstarts : Housing Starts

  • Icecream : Ice Cream Consumption

  • IncomeUK : Seasonally Unadjusted Quarterly Data on Disposable Income and Expenditure

  • Irates : Monthly Interest Rates

  • LT : Dollar Sterling Exchange Rate

  • MW : Growth of Disposable Income and Treasury Bill Rate

  • Macrodat : Macroeconomic Time Series for the United States

  • Mishkin : Inflation and Interest Rates

  • MoneyUS : Macroeconomic Series for the United States

  • Mpyr : Money, National Product and Interest Rate

  • Orange : The Orange Juice Data Set

  • PE : Price and Earnings Index

  • PPP : Exchange Rates and Price Indices for France and Italy

  • Pound : Pound-dollar Exchange Rate

  • Pricing : Returns of Size-based Portfolios

  • Solow : Solow's Technological Change Data

  • Tbrate : Interest Rate, GDP and Inflation

  • Yen : Yen-dollar Exchange Rate

Description

• consumer behavior

  • BudgetFood : Budget Share of Food for Spanish Households

  • BudgetItaly : Budget Shares for Italian Households

  • BudgetUK : Budget Shares of British Households

  • Car : Stated Preferences for Car Choice

  • Cigar : Cigarette Consumption

  • Cigarette : The Cigarette Consumption Panel Data Set

  • Doctor : Number of Doctor Visits

  • Fishing : Choice of Fishing Mode

  • Gasoline : Gasoline Consumption

  • HC : Heating and Cooling System Choice in Newly Built Houses in California

  • Heating : Heating System Choice in California Houses

  • Icecream : Ice Cream Consumption

  • Mode : Mode Choice

  • ModeChoice : Data to Study Travel Mode Choice

  • Somerville : Visits to Lake Somerville

  • Tobacco : Households Tobacco Budget Share

  • Train : Stated Preferences for Train Traveling

• economics of education

  • Caschool : The California Test Score Data Set

  • MCAS : The Massachusetts Test Score Data Set

  • Mathlevel : Level of Calculus Attained for Students Taking Advanced Micro–economics

  • Star : Effects on Learning of Small Class Sizes

• environmental economics

  • Airq : Air Quality for Californian Metropolitan Areas

  • Kakadu : Willingness to Pay for the Preservation of the Kakadu National Park

  • NaturalPark : Willingness to Pay for the Preservation of the Alentejo Natural Park

• finance

  • CRSPday : Daily Returns from the CRSP Database

  • CRSPmon : Monthly Returns from the CRSP Database

  • Capm : Stock Market Data

  • DM : DM Dollar Exchange Rate

  • Forward : Exchange Rates of US Dollar Against Other Currencies

  • Garch : Daily Observations on Exchange Rates of the US Dollar Against Other Currencies

  • Irates : Monthly Interest Rates

  • LT : Dollar Sterling Exchange Rate

  • PPP : Exchange Rates and Price Indices for France and Italy

  • Pound : Pound-dollar Exchange Rate

  • Pricing : Returns of Size-based Portfolios

  • Yen : Yen-dollar Exchange Rate

• game theory

  • FriendFoe : Data from the Television Game Show Friend Or Foe ?

• health economics

  • DoctorAUS : Doctor Visits in Australia

  • DoctorContacts : Contacts With Medical Doctor

  • MedExp : Structure of Demand for Medical Care

  • OFP : Visits to Physician Office

  • VietNamH : Medical Expenses in Vietnam (household Level)

  • VietNamI : Medical Expenses in Vietnam (individual Level)

• hedonic prices

  • Computers : Prices of Personal Computers

  • Diamond : Pricing the C's of Diamond Stones

  • Hedonic : Hedonic Prices of Census Tracts in Boston

  • Housing : Sales Prices of Houses in the City of Windsor

  • Journals : Economic Journals Data Set

• labor economics

  • Benefits : Unemployment of Blue Collar Workers

  • Bwages : Wages in Belgium

  • CPSch3 : Earnings from the Current Population Survey

  • Earnings : Earnings for Three Age Groups

  • Griliches : Wage Data

  • HI : Health Insurance and Hours Worked By Wives

  • LaborSupply : Wages and Hours Worked

  • Labour : Belgian Firms

  • Males : Wages and Education of Young Males

  • Mroz : Labor Supply Data

  • PSID : Panel Survey of Income Dynamics

  • Participation : Labor Force Participation

  • RetSchool : Return to Schooling

  • Schooling : Wages and Schooling

  • Strike : Strike Duration Data

  • StrikeDur : Strikes Duration

  • StrikeNb : Number of Strikes in Us Manufacturing

  • Treatment : Evaluating Treatment Effect of Training on Earnings

  • UnempDur : Unemployment Duration

  • Unemployment : Unemployment Duration

  • Wages : Panel Data of Individual Wages

  • Wages1 : Wages, Experience and Schooling

  • Workinghours : Wife Working Hours

• macroeconomics

  • Consumption : Quarterly Data on Consumption and Expenditure

  • Hstarts : Housing Starts

  • IncomeUK : Seasonally Unadjusted Quarterly Data on Disposable Income and Expenditure

  • Klein : Klein's Model I

  • Longley : The Longley Data

  • MW : Growth of Disposable Income and Treasury Bill Rate

  • Macrodat : Macroeconomic Time Series for the United States

  • Mishkin : Inflation and Interest Rates

  • Money : Money, GDP and Interest Rate in Canada

  • MoneyUS : Macroeconomic Series for the United States

  • Mpyr : Money, National Product and Interest Rate

  • PE : Price and Earnings Index

  • Produc : Us States Production

  • Solow : Solow's Technological Change Data

  • SumHes : The Penn Table

  • Tbrate : Interest Rate, GDP and Inflation

• marketing

  • Catsup : Choice of Brand for Catsup

  • Cracker : Choice of Brand for Crackers

  • Ketchup : Choice of Brand for Ketchup

  • Tuna : Choice of Brand for Tuna

  • Yogurt : Choice of Brand for Yogurts

• producer behavior

  • Accident : Ship Accidents

  • Airline : Cost for U.S. Airlines

  • Bids : Bids Received By U.S. Firms

  • Clothing : Sales Data of Men's Fashion Stores

  • Electricity : Cost Function for Electricity Producers

  • Grunfeld : Grunfeld Investment Data

  • Hmda : The Boston HMDA Data Set

  • ManufCost : Manufacturing Costs

  • Metal : Production for SIC 33

  • Mofa : International Expansion of U.S. MOFAs (majority–owned Foreign Affiliates in Fire (finance, Insurance and Real Estate)

  • Nerlove : Cost Function for Electricity Producers, 1955

  • Oil : Oil Investment

  • Orange : The Orange Juice Data Set

  • PatentsHGH : Dynamic Relation Between Patents and R&D

  • PatentsRD : Patents, R&D and Technological Spillovers for a Panel of Firms

  • TranspEq : Statewide Data on Transportation Equipment Manufacturing

  • University : Provision of University Teaching and Research

• socioeconomics

  • Crime : Crime in North Carolina

  • Fair : Extramarital Affairs Data

  • Fatality : Drunk Driving Laws and Traffic Deaths

Description

• country

  • Consumption : Quarterly Data on Consumption and Expenditure

  • DM : DM Dollar Exchange Rate

  • Garch : Daily Observations on Exchange Rates of the US Dollar Against Other Currencies

  • Gasoline : Gasoline Consumption

  • Hstarts : Housing Starts

  • Icecream : Ice Cream Consumption

  • IncomeUK : Seasonally Unadjusted Quarterly Data on Disposable Income and Expenditure

  • Irates : Monthly Interest Rates

  • Klein : Klein's Model I

  • LT : Dollar Sterling Exchange Rate

  • Longley : The Longley Data

  • MW : Growth of Disposable Income and Treasury Bill Rate

  • Macrodat : Macroeconomic Time Series for the United States

  • ManufCost : Manufacturing Costs

  • Mishkin : Inflation and Interest Rates

  • Mofa : International Expansion of U.S. MOFAs (majority–owned Foreign Affiliates in Fire (finance, Insurance and Real Estate)

  • Money : Money, GDP and Interest Rate in Canada

  • Mpyr : Money, National Product and Interest Rate

  • Orange : The Orange Juice Data Set

  • PE : Price and Earnings Index

  • PPP : Exchange Rates and Price Indices for France and Italy

  • Pound : Pound-dollar Exchange Rate

  • Solow : Solow's Technological Change Data

  • StrikeNb : Number of Strikes in Us Manufacturing

  • SumHes : The Penn Table

  • Tbrate : Interest Rate, GDP and Inflation

  • Yen : Yen-dollar Exchange Rate

• goods

  • Computers : Prices of Personal Computers

  • Diamond : Pricing the C's of Diamond Stones

  • Housing : Sales Prices of Houses in the City of Windsor

  • Journals : Economic Journals Data Set

• households

  • BudgetFood : Budget Share of Food for Spanish Households

  • BudgetItaly : Budget Shares for Italian Households

  • BudgetUK : Budget Shares of British Households

  • HC : Heating and Cooling System Choice in Newly Built Houses in California

  • Heating : Heating System Choice in California Houses

  • VietNamH : Medical Expenses in Vietnam (household Level)

• individuals

  • Benefits : Unemployment of Blue Collar Workers

  • Bwages : Wages in Belgium

  • CPSch3 : Earnings from the Current Population Survey

  • Car : Stated Preferences for Car Choice

  • Catsup : Choice of Brand for Catsup

  • Cracker : Choice of Brand for Crackers

  • Doctor : Number of Doctor Visits

  • DoctorAUS : Doctor Visits in Australia

  • Earnings : Earnings for Three Age Groups

  • Fair : Extramarital Affairs Data

  • Fishing : Choice of Fishing Mode

  • FriendFoe : Data from the Television Game Show Friend Or Foe ?

  • Griliches : Wage Data

  • HI : Health Insurance and Hours Worked By Wives

  • Hmda : The Boston HMDA Data Set

  • Kakadu : Willingness to Pay for the Preservation of the Kakadu National Park

  • Ketchup : Choice of Brand for Ketchup

  • Males : Wages and Education of Young Males

  • Mathlevel : Level of Calculus Attained for Students Taking Advanced Micro–economics

  • Mode : Mode Choice

  • ModeChoice : Data to Study Travel Mode Choice

  • Mroz : Labor Supply Data

  • NaturalPark : Willingness to Pay for the Preservation of the Alentejo Natural Park

  • OFP : Visits to Physician Office

  • PSID : Panel Survey of Income Dynamics

  • Participation : Labor Force Participation

  • RetSchool : Return to Schooling

  • Schooling : Wages and Schooling

  • Somerville : Visits to Lake Somerville

  • Star : Effects on Learning of Small Class Sizes

  • Tobacco : Households Tobacco Budget Share

  • Train : Stated Preferences for Train Traveling

  • Tuna : Choice of Brand for Tuna

  • Unemployment : Unemployment Duration

  • VietNamI : Medical Expenses in Vietnam (individual Level)

  • Wages : Panel Data of Individual Wages

  • Wages1 : Wages, Experience and Schooling

  • Workinghours : Wife Working Hours

  • Yogurt : Choice of Brand for Yogurts

• production units

  • Airline : Cost for U.S. Airlines

  • Bids : Bids Received By U.S. Firms

  • CRSPday : Daily Returns from the CRSP Database

  • CRSPmon : Monthly Returns from the CRSP Database

  • Clothing : Sales Data of Men's Fashion Stores

  • Electricity : Cost Function for Electricity Producers

  • Grunfeld : Grunfeld Investment Data

  • Labour : Belgian Firms

  • Nerlove : Cost Function for Electricity Producers, 1955

  • Oil : Oil Investment

  • PatentsHGH : Dynamic Relation Between Patents and R&D

  • PatentsRD : Patents, R&D and Technological Spillovers for a Panel of Firms

• regional

  • Airq : Air Quality for Californian Metropolitan Areas

  • Cigar : Cigarette Consumption

  • Cigarette : The Cigarette Consumption Panel Data Set

  • Crime : Crime in North Carolina

  • Fatality : Drunk Driving Laws and Traffic Deaths

  • Hedonic : Hedonic Prices of Census Tracts in Boston

  • Metal : Production for SIC 33

  • MunExp : Municipal Expenditure Data

  • Produc : Us States Production

  • TranspEq : Statewide Data on Transportation Equipment Manufacturing

• schools

  • Caschool : The California Test Score Data Set

  • MCAS : The Massachusetts Test Score Data Set

  • University : Provision of University Teaching and Research

Description

• Journal of Applied Econometrics data archive : http://qed.econ.queensu.ca/jae/

  • Bids : Bids Received By U.S. Firms

  • BudgetFood : Budget Share of Food for Spanish Households

  • BudgetItaly : Budget Shares for Italian Households

  • BudgetUK : Budget Shares of British Households

  • Car : Stated Preferences for Car Choice

  • Computers : Prices of Personal Computers

  • Crime : Crime in North Carolina

  • Doctor : Number of Doctor Visits

  • Earnings : Earnings for Three Age Groups

  • HI : Health Insurance and Hours Worked By Wives

  • Housing : Sales Prices of Houses in the City of Windsor

  • Males : Wages and Education of Young Males

  • Mathlevel : Level of Calculus Attained for Students Taking Advanced Micro–economics

  • MoneyUS : Macroeconomic Series for the United States

  • MunExp : Municipal Expenditure Data

  • OFP : Visits to Physician Office

  • Oil : Oil Investment

  • Participation : Labor Force Participation

  • PatentsRD : Patents, R&D and Technological Spillovers for a Panel of Firms

  • Train : Stated Preferences for Train Traveling

  • Unemployment : Unemployment Duration

  • University : Provision of University Teaching and Research

  • Workinghours : Wife Working Hours

• Journal of Business Economics and Statistics web site : https://amstat.tandfonline.com/loi/ubes20

  • Benefits : Unemployment of Blue Collar Workers

  • Catsup : Choice of Brand for Catsup

  • Cracker : Choice of Brand for Crackers

  • Kakadu : Willingness to Pay for the Preservation of the Kakadu National Park

  • Ketchup : Choice of Brand for Ketchup

  • LaborSupply : Wages and Hours Worked

  • Mofa : International Expansion of U.S. MOFAs (majority–owned Foreign Affiliates in Fire (finance, Insurance and Real Estate)

  • Somerville : Visits to Lake Somerville

  • Tuna : Choice of Brand for Tuna

  • Yogurt : Choice of Brand for Yogurts

• Journal of Statistics Education's data archive : http://jse.amstat.org/jse_data_archive.htm

  • Diamond : Pricing the C's of Diamond Stones

  • FriendFoe : Data from the Television Game Show Friend Or Foe ?

• Kenneth Train's home page : https://eml.berkeley.edu/~train/

  • HC : Heating and Cooling System Choice in Newly Built Houses in California

  • Heating : Heating System Choice in California Houses

  • Mode : Mode Choice

• Baltagi, Badi H. (2003) Econometric analysis of panel data, John Wiley and sons, https://www.wiley.com/legacy/wileychi/baltagi/

  • Cigar : Cigarette Consumption

  • Crime : Crime in North Carolina

  • Gasoline : Gasoline Consumption

  • Grunfeld : Grunfeld Investment Data

  • Hedonic : Hedonic Prices of Census Tracts in Boston

  • Produc : Us States Production

  • Wages : Panel Data of Individual Wages

• Cameron, A.C. and P.K. Trivedi (2005) Microeconometrics : methods and applications, Cambridge

  • DoctorContacts : Contacts With Medical Doctor

  • Fishing : Choice of Fishing Mode

  • LaborSupply : Wages and Hours Worked

  • MedExp : Structure of Demand for Medical Care

  • PSID : Panel Survey of Income Dynamics

  • PatentsHGH : Dynamic Relation Between Patents and R&D

  • RetSchool : Return to Schooling

  • StrikeDur : Strikes Duration

  • Treatment : Evaluating Treatment Effect of Training on Earnings

  • UnempDur : Unemployment Duration

  • VietNamH : Medical Expenses in Vietnam (household Level)

  • VietNamI : Medical Expenses in Vietnam (individual Level)

• Cameron, A.C. and Trivedi P.K. (1998) Regression analysis of count data, Cambridge University Press, http://cameron.econ.ucdavis.edu/racd/racddata.html

  • Bids : Bids Received By U.S. Firms

  • DoctorAUS : Doctor Visits in Australia

  • OFP : Visits to Physician Office

  • PatentsHGH : Dynamic Relation Between Patents and R&D

  • Somerville : Visits to Lake Somerville

  • StrikeNb : Number of Strikes in Us Manufacturing

• Davidson, R. and James G. MacKinnon (2004) Econometric Theory and Methods, New York, Oxford University Press

  • CRSPday : Daily Returns from the CRSP Database

  • CRSPmon : Monthly Returns from the CRSP Database

  • Consumption : Quarterly Data on Consumption and Expenditure

  • Doctor : Number of Doctor Visits

  • Earnings : Earnings for Three Age Groups

  • Hstarts : Housing Starts

  • MW : Growth of Disposable Income and Treasury Bill Rate

  • Money : Money, GDP and Interest Rate in Canada

  • Participation : Labor Force Participation

  • Tbrate : Interest Rate, GDP and Inflation

• Greene, W.H. (2003) Econometric Analysis, Prentice Hall

  • Accident : Ship Accidents

  • Airline : Cost for U.S. Airlines

  • Electricity : Cost Function for Electricity Producers

  • Fair : Extramarital Affairs Data

  • Grunfeld : Grunfeld Investment Data

  • Klein : Klein's Model I

  • Longley : The Longley Data

  • ManufCost : Manufacturing Costs

  • Metal : Production for SIC 33

  • ModeChoice : Data to Study Travel Mode Choice

  • Mroz : Labor Supply Data

  • MunExp : Municipal Expenditure Data

  • Nerlove : Cost Function for Electricity Producers, 1955

  • Solow : Solow's Technological Change Data

  • Strike : Strike Duration Data

  • TranspEq : Statewide Data on Transportation Equipment Manufacturing

• Hayashi, F. (2000) Econometrics, Princeton University Press, http://fhayashi.fc2web.com/hayashi_econometrics.htm

  • DM : DM Dollar Exchange Rate

  • Electricity : Cost Function for Electricity Producers

  • Griliches : Wage Data

  • LT : Dollar Sterling Exchange Rate

  • Mishkin : Inflation and Interest Rates

  • Mpyr : Money, National Product and Interest Rate

  • Nerlove : Cost Function for Electricity Producers, 1955

  • Pound : Pound-dollar Exchange Rate

  • SumHes : The Penn Table

  • Yen : Yen-dollar Exchange Rate

• Stock, James H. and Mark W. Watson (2003) Introduction to Econometrics, Addison-Wesley Educational Publishers

  • CPSch3 : Earnings from the Current Population Survey

  • Caschool : The California Test Score Data Set

  • Cigarette : The Cigarette Consumption Panel Data Set

  • Fatality : Drunk Driving Laws and Traffic Deaths

  • Hmda : The Boston HMDA Data Set

  • Journals : Economic Journals Data Set

  • MCAS : The Massachusetts Test Score Data Set

  • Macrodat : Macroeconomic Time Series for the United States

  • Orange : The Orange Juice Data Set

  • Star : Effects on Learning of Small Class Sizes

• Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons

  • Airq : Air Quality for Californian Metropolitan Areas

  • Benefits : Unemployment of Blue Collar Workers

  • Bwages : Wages in Belgium

  • Capm : Stock Market Data

  • Clothing : Sales Data of Men's Fashion Stores

  • Forward : Exchange Rates of US Dollar Against Other Currencies

  • Garch : Daily Observations on Exchange Rates of the US Dollar Against Other Currencies

  • Housing : Sales Prices of Houses in the City of Windsor

  • Icecream : Ice Cream Consumption

  • IncomeUK : Seasonally Unadjusted Quarterly Data on Disposable Income and Expenditure

  • Irates : Monthly Interest Rates

  • Labour : Belgian Firms

  • Males : Wages and Education of Young Males

  • MoneyUS : Macroeconomic Series for the United States

  • NaturalPark : Willingness to Pay for the Preservation of the Alentejo Natural Park

  • PE : Price and Earnings Index

  • PPP : Exchange Rates and Price Indices for France and Italy

  • PatentsRD : Patents, R&D and Technological Spillovers for a Panel of Firms

  • Pricing : Returns of Size-based Portfolios

  • SP500 : Returns on Standard & Poor's 500 Index

  • Schooling : Wages and Schooling

  • Tobacco : Households Tobacco Budget Share

  • Wages1 : Wages, Experience and Schooling

Description

• annual

  • Klein : Klein's Model I

  • LT : Dollar Sterling Exchange Rate

  • Longley : The Longley Data

  • ManufCost : Manufacturing Costs

  • Mpyr : Money, National Product and Interest Rate

  • PE : Price and Earnings Index

  • Solow : Solow's Technological Change Data

• daily

  • CRSPday : Daily Returns from the CRSP Database

  • Garch : Daily Observations on Exchange Rates of the US Dollar Against Other Currencies

  • SP500 : Returns on Standard & Poor's 500 Index

• four–weekly

  • Icecream : Ice Cream Consumption

• monthly

  • CRSPmon : Monthly Returns from the CRSP Database

  • Capm : Stock Market Data

  • Forward : Exchange Rates of US Dollar Against Other Currencies

  • Irates : Monthly Interest Rates

  • Mishkin : Inflation and Interest Rates

  • Orange : The Orange Juice Data Set

  • PPP : Exchange Rates and Price Indices for France and Italy

  • Pricing : Returns of Size-based Portfolios

  • StrikeNb : Number of Strikes in Us Manufacturing

• quarterly

  • Consumption : Quarterly Data on Consumption and Expenditure

  • Hstarts : Housing Starts

  • IncomeUK : Seasonally Unadjusted Quarterly Data on Disposable Income and Expenditure

  • MW : Growth of Disposable Income and Treasury Bill Rate

  • Macrodat : Macroeconomic Time Series for the United States

  • Money : Money, GDP and Interest Rate in Canada

  • MoneyUS : Macroeconomic Series for the United States

  • Tbrate : Interest Rate, GDP and Inflation

• weekly

  • DM : DM Dollar Exchange Rate

  • Pound : Pound-dollar Exchange Rate

  • Yen : Yen-dollar Exchange Rate

Description

monthly observations from 1946–12 to 1991–02

number of observations : 531

observation : country

country : United–States

Usage

data(Irates)

Format

A time series containing :

r1

interest rate for a maturity of 1 months (% per year).

r2

interest rate for a maturity of 2 months (% per year).

r3

interest rate for a maturity of 3 months (% per year).

r5

interest rate for a maturity of 5 months (% per year).

r6

interest rate for a maturity of 6 months (% per year).

r11

interest rate for a maturity of 11 months (% per year).

r12

interest rate for a maturity of 12 months (% per year).

r36

interest rate for a maturity of 36 months (% per year).

r60

interest rate for a maturity of 60 months (% per year).

r120

interest rate for a maturity of 120 months (% per year).

Source

McCulloch, J.H. and H.C. Kwon (1993) U.S. term structure data, 1947–1991, Ohio State Working Paper 93-6, Ohio State University, Columbus.

References

Verbeek, Marno (2004) A Guide to Modern Econometrics, John Wiley and Sons, chapter 8.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations,

Index.Time.Series

Description

a cross-section from 2000

number of observations : 180

observation : goods

Usage

data(Journals)

Format

A dataframe containing :

title

journal title

pub

publisher

society

scholarly society ?

libprice

library subscription price

pages

number of pages

charpp

characters per page

citestot

total number of citations

date1

year journal was founded

oclc

number of library subscriptions

field

field description

Source

Professor Theodore Bergstrom of the Department of Economics at the University of California, San Diego.

References

Stock, James H. and Mark W. Watson (2003) Introduction to Econometrics, Addison-Wesley Educational Publishers, chapter 6.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section

number of observations : 1827

observation : individuals

country : Australia

Usage

data(Kakadu)

Format

A dataframe containing :

lower

lower bound of willingness to pay, 0 if observation is left censored

upper

upper bound of willingness to pay, 999 if observation is right censored

answer

an ordered factor with levels nn (respondent answers no, no), ny (respondent answers no, yes or yes, no), yy (respondent answers yes, yes)

recparks

the greatest value of national parks and nature reserves is in recreation activities (from 1 to 5)

jobs

jobs are the most important thing in deciding how to use our natural resources (from 1 to 5)

lowrisk

development should be allowed to proceed where environmental damage from activities such as mining is possible but very unlikely (from 1 to 5)

wildlife

it's important to have places where wildlife is preserved (from 1 to 5)

future

it's important to consider future generations (from 1 to 5)

aboriginal

in deciding how to use areas such as Kakadu national park, their importance to the local aboriginal people should be a major factor (from 1 to 5)

finben

in deciding how to use our natural resources such as mineral deposits and forests, the most important thing is the financial benefits for Australia (from 1 to 5)

mineparks

if areas within natural parks are set aside for development projects such as mining, the value of the parks is greatly reduced (from 1 to 5)

moreparks

there should be more national parks created from state forests (from 1 to 5)

gov

the government pays little attention to the people in making decisions (from 1 to 4)

envcon

the respondent recycles things such as paper or glass and regularly buys unbleached toilet paper or environmentally friendly products?

vparks

the respondent has visited a national park or bushland recreation area in the previous 12 months?

tvenv

the respondent watches TV programs about the environment? (from 1 to 9)

conservation

the respondent is member of a conservation organization?

sex

male,female

age

age

schooling

years of schooling

income

respondent's income in thousands of dollars

major

the respondent received the major–impact scenario of the Kakadu conservation zone survey ?

Source

Werner, Megan (1999) “Allowing for zeros in dichotomous–choice contingent–valuation models”, Journal of Business and Economic Statistics, 17(4), October, 479–486.

References

Journal of Business Economics and Statistics web site : https://amstat.tandfonline.com/loi/ubes20.

See Also

Index.Source, Index.Economics, Index.Econometrics, Index.Observations

Description

a cross-section

number of observations : 4956

observation : individuals

country : United States

Usage

data(Ketchup)

Format

A dataframe containing :

hid

individuals identifiers

id

purchase identifiers

choice

one of heinz, hunts, delmonte, stb (store brand)

price.z

price of brand z

Source