R/documentation.R

In RDHonest: Honest Inference in Regression Discontinuity Designs

# nolint start
#' Head Start data from Ludwig and Miller (2007)
#'
#' Subset of Ludwig-Miller (2007) data. Counties with missing poverty rate, or
#' with both outcomes missing (\code{hs} and \code{mortality}) were removed. In
#' the original dataset, Yellowstone County, MT (\code{oldcode = 27056}) was
#' entered twice, here the duplicate is removed. Yellowstone National Park, MT
#' (\code{oldcode = 27057}) is also removed due to it being an outlier for both
#' outcomes. Counties with \code{oldcode} equal to (3014, 32032, 47010, 47040,
#' 47074, 47074, 47078, 47079, 47096) matched more than one FIPS entry, so the
#' county labels may not be correct. Mortality data is missing for Alaska.
#' @format A data frame with 3,127 rows and 18 variables:
#'
#' \describe{
#' \item{statefp}{State FIPS code}
#' \item{countyfp}{County FIPS code}
#' \item{oldcode}{ID in Ludwig-Miller dataset}
#' \item{povrate}{Poverty rate in 1960 relative to 300th poorest county (which
#'      had poverty rate 59.1984)}
#' \item{mortHS}{Average Mortality rate per 100,000 for children aged 5-9 over
#' 1973--83 due to causes addressed as part of Head Start's health services}
#'
#' \item{mortInj}{Average Mortality rate per 100,000 for children aged 5-9 over
#' 1973--83 due to injury}
#' \item{hs90}{High school completion rate in 1990 census, ages 18-24}
#' \item{pop}{County population (1960 census)}
#' \item{sch1417}{Percent attending school, ages 14-17 (1960 census)}
#' \item{sch534}{Percent attending school, ages 5-34 (1960 census)}
#' \item{hs60}{High school completion rate in 1960 census, ages 25+}
#' \item{pop1417}{Population aged 14-17 (1960 census)}
#' \item{pop534}{Population aged 5-34 (1960 census)}
#' \item{pop25}{Population aged 25+ (1960 census)}
#' \item{urban}{Percent urban (1960 census)}
#' \item{black}{Percent black (1960 census)}
#' \item{statepc}{State postal code}
#' \item{county}{County name}
#' }
#' @source Douglas Miller's former website,
#' \url{http://web.archive.org/web/20190619165949/http://faculty.econ.ucdavis.edu:80/faculty/dlmiller/statafiles/}
#' @references{
#'
#' \cite{Jens Ludwig and Douglas L. Miller. Does head start improve children's
#'       life chances? Evidence from a regression discontinuity design.
#'       Quarterly Journal of Economics, 122(1):159–208, February 2007.
#'       \doi{10.1162/qjec.122.1.159}}
#'
#' }
"headst"
# nolint end

#' Austrian unemployment duration data from Lalive (2008)
#'
#' Subset of Lalive (2008) data for individuals in the regions affected by the
#' REBP program
#'
#' @format A data frame with 29,371 rows and 4 variables:
#' \describe{
#'   \item{age}{Age in years, at monthly accuracy}
#'   \item{period}{Indicator for whether REBP is in place}
#'   \item{female}{Indicator for female}
#'   \item{duration}{unemployment duration in weeks}
#'  }
#' @references{
#'
#' \cite{Rafael Lalive. How do extended benefits affect unemployment duration? A
#'       regression discontinuity approach. Journal of Econometrics,
#'       142(2):785–806, February 2008. \doi{10.1016/j.jeconom.2007.05.013}}
#'
#' }
#' @source Rafael Lalive's website,
#'     \url{https://sites.google.com/site/rafaellalive/}
"rebp"


#' Lee (2008) US House elections dataset
#'
#' @format A data frame with 6,558 rows and 2 variables:
#' \describe{
#'   \item{voteshare}{Vote share in next election}
#'   \item{margin}{Democratic margin of victory}
#'  }
#' @source Mostly Harmless Econometrics data archive,
#' \url{https://economics.mit.edu/people/faculty/josh-angrist/mhe-data-archive}
#' @references{
#'
#' \cite{David S. Lee. Randomized experiments from non-random selection in U.S.
#'       House elections. Journal of Econometrics, 142(2):675–697, 2008.
#'       \doi{10.1016/j.jeconom.2007.05.004}}
#'
#' }
"lee08"

#' Oreopoulos (2006) UK general household survey dataset
#'
#' @format A data frame with 73,954 rows and 2 variables:
#' \describe{
#'   \item{earnings}{Annual earnings in 1998 (UK pounds)}
#'   \item{yearat14}{Year individual turned 14}
#'  }
#' @references{
#'
#' \cite{Philip Oreopoulos. Estimating average and local average treatment
#'       effects when compulsory education schooling laws really matter.
#'       American Economic Review, 96(1):152–175, 2006.
#'       \doi{10.1257/000282806776157641}}
#'
#' }
#' @source American Economic Review data archive,
#' \doi{10.1257/000282806776157641}
"cghs"

#' Battistin, Brugiavini, Rettore, and Weber (2009) retirement consumption
#' puzzle dataset
#'
#' @format A data frame with 30,006 rows and 6 variables:
#' \describe{
#'   \item{survey_year}{Survey year}
#'   \item{elig_year}{Years to/from eligibility (males)}
#'   \item{retired}{Retirement status (males)}
#'   \item{food}{Total household food expenditure}
#'   \item{c}{Total household consumption}
#'   \item{cn}{Total household expenditure on non-durable goods}
#'   \item{education}{Educational attainment (males), one of: "none",
#'         "elementary school", "lower secondary", "vocational studies",
#'         "upper secondary", "college or higher")}
#'   \item{family_size}{Family size}
#' }
#'
#' @references{
#'
#' \cite{Erich Battistin, Agar Brugiavini, Enrico Rettore, and Guglielmo Weber.
#'      The retirement consumption puzzle: Evidence from a regression
#'      discontinuity approach. American Economic Review, 99(5):2209–2226, 2009.
#'      \doi{10.1257/aer.99.5.2209}}
#'
#' }
#' @source American Economic Review data archive, \doi{10.1257/aer.99.5.2209}
"rcp"

## Constants for common kernels.
##
## First four moments of uniform, triangular, and Epanechnikov equivalent
## kernels.
##
## @format A data frame with 18 rows and 19 variables:
## \describe{
##   \item{kernel}{Kernel type.}
##
##   \item{order}{Order of local polynomial.}
##
##   \item{boundary}{Boundary regression?}
##
##   \item{mu0, mu1, mu2, mu3, mu4}{\eqn{\int_X u^j k(u) d u}, raw moments}
##
##   \item{nu0, nu1, nu2, nu3, nu4}{\eqn{\int_X u^j k^2(u) d u}, raw moments of
##         kernel squared}
##
##   \item{pi0, pi1, pi2, pi3, pi4}{\eqn{\int_X |u^j k(u)| d u}, absolute
##         moments}
##
##   \item{pMSE}{constant for pointwise MSE optimal bandwidth,
##        \eqn{((p+1)!^2\nu_0 / (2(p+1)\mu_{p+1}^2))^{1/(2p+3)}}, see page 67 in
##        Fan and Gijbels (1996)}
##  }
## @source Computed analytically using symbolic math software
## @references{
##
## \cite{Jianqing Fan and Irène Gijbels. Local Polynomial Modelling and Its
##       Applications. Number 66 in Monographs on Statistics and Applied
##       Probability. Chapman & Hall/CRC, New York, NY, 1996.
##       \doi{10.1201/9780203748725}}
##
## }
## "kernC"