Auction data

Description

Data on eBay auctions, based upon the paper "Econometrics of Auctions by Least Squares" by Leonardo Rezende, Journal of Applied Econometrics, 2008, 23:925-948. The dataset consists of eBay auctions for Apple iPod mini devices in June and July 2006, limited to only auctions for the 4GB models.

Usage

auctions

Format

auctions

A data frame with 684 rows and 14 columns:

ebay_auction_id

eBay auction ID number

bidders

Number of bidders

finalprice

Final sales price

seller_feedback_pct

Seller's positive feedback percentage (e.g., 90 = 90%)

seller_feedback_score

Seller's feedback score (number of feedbacks received)

reserveprice

Reserve price set by seller (value of 0.01 if no reserve price)

color_pink

1 if iPod is pink, 0 otherwise

color_blue

1 if iPod is blue, 0 otherwise

color_silver

1 if iPod is silver, 0 otherwise

color_green

1 if iPod is green, 0 otherwise

color_other

1 if iPod is another color, 0 otherwise

new

1 if condition listed is new, 0 otherwise

used

1 if condition listed is used, 0 otherwise

refurb

1 if condition listed is refurbished, 0 otherwise

Source

https://journaldata.zbw.eu/dataset/econometrics-of-auctions-by-least-squares

Popular names data

Description

Data on the names of all babies born in the United States in 2022, as provided by the Social Security Administration. Each observation corresponds to a specific name and gender, with a count of that name provided. For confidentiality reasons, the minimum count for any name is 5. All other names (with fewer than 5 occurrences in the U.S.) are included within the observation having "OTHER" as the name. There are two "OTHER" observations, one for female babies and one for male babies. Data are sorted alphabetically by name.

Usage

babynames

Format

babynames

A data frame with 31915 rows and 3 columns:

name

Baby's name

gender

F if female, M if male

count

Number of babies with name and gender

Source

https://www.ssa.gov/oact/babynames/limits.html

Baseball attendance data

Description

Data on 2022 attendance for Major League Baseball teams

Usage

baseball

Format

baseball

A data frame with 30 rows and 9 columns:

team

Team name

attend_home

Average home game attendance

attend_road

Average road game attendance

winpct_22

Team winning percentage in 2022

winpct_21

Team winning percentage in 2021

playoff_21

1 if team made playoffs in 2021, 0 otherwise

capacity

Capacity of home stadium

popul

Population of team's metropolitan area (2020)

payroll

Total team payroll in 2022 (in millions of dollars)

Source

various

Birth outcome data

Description

Data on birth outcomes in the United States for December 2021 births where mother's age is between 25 and 35 (inclusive), limited to singleton births, mother's first child, and having non-missing values for relevant variables

Usage

births

Format

births

A data frame with 50,249 rows and 20 columns:

birthtime

Birth time during day (in minutes, range is 0 to 2399)

birthwkday

Day of week of birth (1=Sunday, 2=Monday, ..., 7=Saturday)

age

Mother's age (in years)

nonhsgrad

1 if mother is not a HS graduate, 0 otherwise

hsgrad

1 if mother is HS graduate and has no add'l education, 0 otherwise

somecoll

1 if mother completed some college, 0 otherwise

collgrad

1 if mother is 4-year college graduate, 0 otherwise

married

1 if mother is married, 0 otherwise

smoke1

1 if mother smoked during first trimester, 0 otherwise

smoke2

1 if mother smoked during second trimester, 0 otherwise

smoke3

1 if mother smoked during third trimester, 0 otherwise

smokepre

1 if mother smoked before pregnancy, 0 otherwise

smoke

1 if mother smoked during pregnancy (any trimester), 0 otherwise

prenatal1

1 if first prenatal care during first trimester, 0 otherwise

prenatal2

1 if first prenatal care during second trimester, 0 otherwise

prenatal3

1 if first prenatal care during third trimester, 0 otherwise

nocare

1 if no prenatal care visit, 0 otherwise

male

1 if baby is a boy, 0 otherwise

bweight

Birthweight (in grams)

bweight_lbs

Birthweight (in pounds)

Source

https://www.nber.org/research/data/vital-statistics-natality-birth-data

Bitcoin price and returns data

Description

Data on daily prices and returns for Bitcoin during 2020 and 2021

Usage

bitcoin

Format

bitcoin

A data frame with 364 rows and 268 columns:

date

Date

high

Highest price (in dollars)

low

Lowest price (in dollars)

close

End-of-day price (in dollars)

return

Daily return, based on end-of-day prices

Source

https://finance.yahoo.com

Brand data

Description

Data on the purchase behavior of customers at a specific market. The dataset consists of customers who purchased one of five candy-bar brands in their previous visit to the market and records whether or not they make a purchase during this visit and, if so, which brand they purchase. The dataset is adapted from the full dataset that is referenced in the source citation.

Usage

brands

Format

brands

A data frame with 14,560 rows and 3 columns:

purchase

1 if customer makes a purchase, 0 otherwise

brand

Brand purchased (1 through 5), 0 if no purchase

last_brand

Brand purchased (1 through 5) during last visit

Source

https://medium.com/%40miradzji/purchase-probability-analysis-in-certain-market-segments-with-python-b346654ea5ec

State-level cigarette price and tax data

Description

Data on cigarette prices and taxes in 2019 for the 50 U.S. states plus the District of Columbia

Usage

cigdata

Format

cigdata

A data frame with 51 rows and 9 columns:

state

State abbreviation

statename

State name

cigprice

Average price per pack (in dollars)

cigsales

Annual sales, packs per capita

cig_tax_revenue

Total annual tax revenue (in dollars)

cigtax

State tax per pack (in dollars)

producer

1 if tobacco production > 20m pounds, 0 otherwise

Source

https://healthdata.gov/dataset/The-Tax-Burden-on-Tobacco-1970-2019/etts-u9ii

Congressional election data

Description

Data on congressional election outcomes in the United States between 1948 and 1990, based upon the paper "Do Voters Affect or Elect Policies? Evidence from the U.S. House" by David S. Lee, Enrico Moretti, Matthew J. Butler, 2004, Quarterly Journal of Economics, 119: 807-859. This sample is restricted to elections where (i) the incumbent is running for re-election and (ii) are not running unopposed. There are 9,788 observations available, and demographic variables are available for 6,774 of the observations.

Usage

congress

Format

congress

A data frame with 9,788 rows and 15 columns:

state

State code (ICPSR coding)

district

District code

demvote

Number of votes for Democrat candidate

repvote

Number of votes for Republican candidate

year

Year of election

demvoteshare

Percentage of vote for Democrat candidate

lagdemvoteshare

Percentage of vote for Democrat candidate in last election

totpop

Population of Congressional district

medianincome

Median (nominal) income of Congressional district

pcturban

Percentage of Congressional district that is urban

pctblack

Percentage of Congressional district that is black

pcthighschl

Percentage of Congressional district that is HS graduates

votingpop

Voting population of Congressional district

democrat

1 if Democrat wins election (demvoteshare>0.5), 0 otherwise

lagdemocrat

1 if Democrat won last election (lagdemvoteshare>0.5), 0 otherwise

Source

https://eml.berkeley.edu/%7Emoretti/data3.html

Current Population Survey (CPS) data

Description

A subsample of the 2019 Current Population Survey (CPS) consisting of data on individuals aged 30 to 59 (inclusive)

Usage

cps

Format

cps

A data frame with 4,013 rows and 17 columns:

statefips

Two-character state code, including DC

gender

Gender (Male, Female)

metro

Metropolitan-area (Metro, Non-Metro)

race

Race category (Black, White, Other)

hispanic

Hispanic (Hispanic, Non-hispanic)

marstatus

Marital status (Married, Divorced, Widowed, Never married)

lfstatus

Labor-force status (Employed, Unemployed, Not in LF)

ottipcomm

Earnings include overtime, tips, and/or commissions (Yes, No)

hourly

Hourly-worker status (Hourly, Non-hourly)

unionstatus

Union status (Union, Non-union)

age

Age (in years)

hrslastwk

Hours worked last week

unempwks

Number of weeks unemployed

wagehr

Hourly wage (in dollars); only for hourly employees

earnwk

Earnings last week (in dollars)

ownchild

Number of children in household

educ

Highest education level attained (in years)

Source

https://www.census.gov/programs-surveys/cps/data/datasets.html

Dictator-game data

Description

Data on the results from "dictator games" played in an experimental study, based on the paper "Giving and taking in dictator games – differences by gender? A replication study of Chowdhury et al.", Journal of Comments and Replications in Economics, 2023. Each observation corresponds to one play of the game. Earnings are for the dictator. Two game variants are the "giving game" (dictator starts with endowment) and "taking game" (recipient starts with endowment).

Usage

dictator

Format

dictator

A data frame with 137 rows and 5 columns:

earnings

Earnings of the dictator (between 0 and 10)

giving

1 if giving game, 0 otherwise

taking

1 if taking game, 0 otherwise

female

1 if dictator is female, 0 otherwise

female_opp

1 if recipient is female, 0 otherwise

Source

https://journaldata.zbw.eu/dataset/giving-and-taking-in-dictator-games-replication

Exam data

Description

Data on two exam scores for 77 university students

Usage

exams

Format

exams

A data frame with 77 rows and 2 columns:

exam1

Score (out of 100) on the first exam

exam2

Score (out of 100) on the second exam

Housing price data

Description

Data on house sales in Ames, Iowa between 2006 and 2010. The dataset is limited to one-family homes with public utilities and excludes new home sales.

Usage

houseprices

Format

houseprices

A data frame with 973 rows and 16 columns:

lotarea

Area of lot (in square feet)

overallqual

Overall home quality (scale 1-10, 10 best)

yearbuilt

Year house was built

yearremodadd

Year house was remodeled (equal to yearbuilt if never)

bsmtfinsf

Area of finished basement (in square feet, 0 if no finished basement)

grlivarea

Total non-basement living area (in square feet)

fullbath

Number of full bathrooms

halfbath

Number of half bathrooms

bedroomabvgr

Number of non-basement bedrooms

totrmsabvgrd

Number of non-basement rooms (not including bathrooms)

fireplaces

Number of fireplaces

garagecars

Size of garage (0 if no garage)

mosold

Month house sold (1=Jan,...,12=Dec)

yrsold

Year house sold

saleprice

Sales price of house (in dollars)

centralair

1 if house has central air, 0 otherwise

Source

https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data

Health-expenditure data

Description

Data on healthcare utilization and expenditures for adults 50 years and older in the United States, taken from the Health and Retirement Study (HRS) and Asset and Health Dynamics Among the Oldest Old (AHEAD). Data was originally used in the paper "On the distribution and dynamics of health care costs" by Eric French and John Bailey Jones, 2004, Journal of Applied Econometrics, 19: 705-721. This dataset is restricted to non-married individuals in the year 2000.

Usage

hrs

Format

hrs

A data frame with 6,052 rows and 14 columns:

age

Age (in years)

assets

Total assets (in dollars); bottom-coded at $20,000

doctor_visits

Number of doctor visits

drug_costs

Drug costs (in dollars)

income

Income (in dollars); bottom-coded at $5,000

hosp_nights

Number of nights spent in hospital

ins_private

1 if insurance is private or employee-provided, 0 otherwise

ins_medicare

1 if insurance is Medicare, 0 otherwise

ins_medicaid

1 if insurance is Medicaid, 0 otherwise

ins_none

1 if no health insurance, 0 otherwise

male

1 if male, 0 otherwise

medical_costs

Total medical costs (in dollars)

nodrug_financial

1 if did not take prescription drugs for financial reasons, 0 otherwise

outofpocket_costs

Total out-of-pocket medical costs (in dollars)

Source

https://journaldata.zbw.eu/dataset/on-the-distribution-and-dynamics-of-health-care-costs

Inflation data

Description

Data on inflation rates for 45 countries for a ten-year period (2010-2019).

Usage

inflation

Format

inflation

A data frame with 450 rows and 3 columns:

country

Country abbreviation

year

Year

inflation

Annual inflation rate (change in CPI)

Source

https://data.oecd.org/price/inflation-cpi.htm

Inflation expectations data

Description

Data on individual inflation expectations, based on the paper: "Measuring consumer uncertainty about future inflation," by Wandi Bruine de Bruin, Charles F. Manski, Giorgio Topa, Wilbert van der Klaauw, 2011, Journal of Applied Econometrics, 26: 454-478. This dataset has only the observations with point estimates of inflation for individuals between 30 and 70 years of age. The survey took place in 2007 and 2008. The actual inflation, for benchmark, was 3.2% in 2006, 2.9% in 2007, and 3.8% in 2008.

Usage

inflation_expectations

Format

inflation_expectations

A data frame with 290 rows and 6 columns:

inflation_pred

Individual prediction of inflation next year (integer; e.g. 10=10%)

age

Age (in years)

finlit_score

Financial literacy test score (out of 12 points)

male

1 if male, 0 otherwise

collgrad

1 if college graduate, 0 otherwise

famincome_hi

1 if family income > $75,000, 0 otherwise

Source

https://journaldata.zbw.eu/dataset/measuring-consumer-uncertainty-about-future-inflation

Test a single linear restriction of a model

Description

linear_combination takes a set of regression results and a vector representing a linear combination of the parameters and returns the estimate, standard error, and p-value for the null hypothesis that the linear combination is equal to zero.

Usage

linear_combination(regresults, R)

Arguments

Value

List with the following values:

• estimate, the point estimate of the linear combination

• se, the standard error of the point estimate

• p_value, the p-value for the null hypothesis that the linear combination is equal to zero

Examples

# test that the returns to one year of education are equal to ten years of age
model <- estimatr::lm_robust(earnwk ~ age + educ, data = cps)
R <- c(0, -10, 1) # 0 * `intercept` - 10 * `age` + 1 * `education`
linear_combination(model, R)

Married-couple data

Description

Data on married couples in the United States from the 2003 Community Tracking Study (CTS) Household Survey.

Usage

married

Format

married

A data frame with 4,126 rows and 11 columns:

age_w

Age of wife (in years)

age_h

Age of husband (in years)

educ_w

Education of wife (in years)

educ_h

Education of husband (in years)

bmi_w

Body mass index of wife (bottom-coded at 18, top-coded at 40)

bmi_h

Body mass index of husband (bottom-coded at 18, top-coded at 40)

smoke_w

1 if wife smokes, 0 otherwise

smoke_h

1 if husband smokes, 0 otherwise

employed_w

1 if wife employed, 0 otherwise

employed_h

1 if husband employed, 0 otherwise

famincome

Annual family income (in dollars, top-coded at $150,000)

Source

https://www.icpsr.umich.edu/web/HMCA/studies/4216

Econometrics course data

Description

Data on performance in a graduate econometrics course, with GRE test information and domestic/international status available.

Usage

metricsgrades

Format

metricsgrades

A data frame with 68 rows and 4 columns:

gre_quant

Score on GRE quantitative test (out of 170)

gre_verbal

Score on GRE verbal test (out of 170)

domestic

1 if domestic student, 0 if international student

total

Overall composite course grade (out of 100 points)

Mutual-fund performance data

Description

Data on mutual funds categorized as "Large Blend Equity" funds by Morningstar, limited to funds in existence for more than 10 years. Data captured 2/28/2023.

Usage

mutualfunds

Format

mutualfunds

A data frame with 208 rows and 11 columns:

name

Name of mutual fund

fund_age

Age of fund (in years)

expense_ratio

Expense ratio (net)

aum

Assets under management (in millions of dollars)

min_investment

Minimum investment level (in dollars)

load

Y if fund has a load (sales charge or fee), N if not

manager_tenure

Tenure of current fund manager (in years)

return_1yr

One-year annualized return

return_3yr

Three-year annualized return

return_5yr

Five-year annualized return

return_10yr

Ten-year annualized return

Source

https://www.fidelity.com

Premier League soccer data

Description

Data on all game results for the 2020 Premier League soccer season. The Premier League consists of 20 teams. Each team plays every other team twice (home and away) during the season, so there are a total of 38 rounds in the season and 380 total games.

Usage

premier

Format

premier

A data frame with 380 rows and 5 columns:

round

Round (values 1 to 38)

hometeam

Home team

awayteam

Away team

homegoals

Number of goals by the home team

awaygoals

Number of goals by the away team

Source

https://en.wikipedia.org/wiki/2020%E2%80%9321_Premier_League

Resume response data

Description

Data on responses to hypothetical resumes that were created for an experimental study, based upon "Ban the Box, Criminal Records, and Racial Discrimination: A Field Experiment" by Amanda Agan and Sonja Starr, 2018, Quarterly Journal of Economics, 133: 191-235. This dataset considers only the subsample from before the ban-the-box initiative.

Usage

resume

Format

resume

A data frame with 7,332 rows and 7 columns:

crime

1 if applicant has criminal record, 0 otherwise

drugcrime

1 if applicant has committed drug crime, 0 otherwise

propertycrime

1 if applicant has committed property crime, 0 otherwise

ged

1 if applicant has GED, 0 otherwise

empgap

1 if applicant has a gap in employment, 0 otherwise

black

1 if applicant is black, 0 otherwise

response

1 if applicant received positive response, 0 otherwise

Source

doi:10.7910/DVN/VPHMNT

Asymptotic Standard Errors

Description

These functions calculate the asymptotic standard errors of common statistical estimates. se_meanx calculates the standard error of the mean, se_sx calculates the standard error of the population standard deviation estimate, and se_rxy calculate the standard error of the correlation estimate between two vectors.

Usage

se_meanx(x, na.rm = FALSE)

se_rxy(x, y, na.rm = FALSE)

se_sx(x, na.rm = FALSE)

Arguments

Value

A number representing the asymptotic standard error of the particular estimate

Examples

# calculate the mean and se of the mean of wage in the cps data
paste(
  "The average wage is",
  mean(cps$wagehr, na.rm = TRUE),
  "with a margin of error of",
  se_meanx(cps$wagehr, na.rm = TRUE)
)

Monthly returns data for S&P 500 companies

Description

Data on monthly returns for S&P 500 companies between Jan 1991 and Apr 2021

Usage

sp500

Format

sp500

A data frame with 364 rows and 268 columns:

Date

Date, as a string, indicating the endpoint of the month

IDX

Monthly return for the S&P 500 index

AAPL, ABMD, ..., ZION

Monthly company returns, where variable name is the company stock ticker symbol

Source

https://finance.yahoo.com

Strike duration data

Description

Data on the length of worker contract strikes within U.S. manufacturing for the period 1968-1976, based upon "The Duration of Contract strikes in U.S. Manufacturing" by John Kennan, 1985, Journal of Econometrics, 28: 5-28.

Usage

strikes

Format

strikes

A data frame with 566 rows and 1 column:

duration

Strike duration (in weeks)

Source

https://cameron.econ.ucdavis.edu/mmabook/mmadata.html

Test multiple linear restrictions simultaneously

Description

test_linear_restrictions takes a set of regression results and tests multiple linear restrictions simultaneously.

Usage

test_linear_restrictions(regresults, R, c = default_test(R))

Arguments

Value

A list with the following items:

• W: The Wald (chi-square) statistic

• p_value: The p-value of the test

Examples

# test both that the returns to one year of education are
# equal to ten years of age, and that the intercept is zero
model <- estimatr::lm_robust(earnwk ~ age + educ, data = cps)
R <- matrix(c(0, -10, 1, 1, 0, 0), nrow = 2, byrow = TRUE)
test_linear_restrictions(model, R)

Variance helper functions

Description

These functions help calculate the variance matrix of different kinds of samples. var_mean_indep creates an asymptotic covariance matrix for the sample means of a list of independent samples. var_prop_indep creates an asymptotic covariance matrix for the sample proportions of a list of independent samples. var_mean_onesample creates an asymptotic covariance matrix for the sample means of several variables from the same sample.

Usage

var_mean_indep(x_vectors)

var_mean_onesample(df, vars = names(df))

var_prop_indep(pi_hat, nobs)

Arguments

Value

A matrix, representing the asymptotic covariance matrix of the sample means.

Examples

# list of independent samples
x_vectors <- list(
  rnorm(1000, mean = 1, sd = 2),
  rnorm(10, mean = 4, sd = 0.5),
  rnorm(1000000, mean = 0, sd = 1)
)
var_mean_indep(x_vectors)

# sample proportions
pi_hat <- c(0.1, 0.6, 0.3)
nobs <- 1000
var_prop_indep(pi_hat, nobs)

# covariance of educ and age in cps dataset
var_mean_onesample(cps, c("educ", "age"))

Wald test statistic and p-value

Description

Given the parameter estimates and their variance-covariance matrix, wald_test calculates the Wald test statistic and p-value for a set of linear constraints on the parameters.

Usage

wald_test(
  gamma_hat,
  var_gamma_hat,
  R = diag(length(gamma_hat)),
  c = default_test(R)
)

Arguments

Value

A list with the following elements:

• W: Wald test statistic

• p_value: p-value for the Wald test (\chi^2_Q distribution)

Examples

# test that union workers earn the same as non-union workers
cps$union <- as.numeric(cps$unionstatus == "Union")
model <- lm(earnwk ~ union, data = cps)
gamma_hat <- coef(model)
var_gamma_hat <- vcov(model)
wald_test(gamma_hat, var_gamma_hat, R = c(0, 1))

# test that non-union workers make 900/week
# *and* union workers make 1000/week
wald_test(
  gamma_hat,
  var_gamma_hat,
  R = matrix(c(0, 1, 1, 1), nrow = 2),
  c = c(900, 1000)
)

Website visitor arrival data

Description

Data on the arrival time of website visitors during a specific hour for a hypothetical website.

Usage

website

Format

website

A data frame with 748 rows and 2 columns:

arrival

Arrival time during the hour (in minutes)

time_since_last

Time since last visitor (in minutes)

Hypothetical data for widgets.com website

Description

Data on purchases for an e-mail experiment run by widgets.com

Usage

widgets

Format

widgets

A data frame with 3,000 rows and 4 columns:

emailA

1 if customer receives e-mail A, 0 otherwise

emailB

1 if customer receives e-mail B, 0 otherwise

purchase

1 if customer makes a purchase, 0 otherwise

amount

Total purchase (in dollars)