plm Package - Linear Models and Tests for Panel Data
plm is a package for panel data econometrics for the
R statistical computing environment. The package
includes functions for model estimation, testing, robust covariance
matrix estimation, panel data manipulation and information. It was first
published on CRAN in 2006.
Be sure to read the NEWS on CRAN for any changes in new releases (new features, bugfixes, other improvements, …).
Non-exhaustive function overview:
Functions to estimate models:
plm: panel data estimators (within/fixed effects,
random effects, between, first-difference, nested random effects),
incl. instrumental-variable estimation techniques (IV) and
Hausman-Taylor-style models,pgmm: generalized method of moments (GMM) estimation
for panel data,pggls: estimation of general feasible generalized least
squares models,pmg: mean groups (MG), demeaned MG and common
correlated effects (CCEMG) estimators,pcce: estimators for common correlated effects mean
groups (CCEMG) and pooled (CCEP) for panel data with common
factors,pvcm: variable coefficients models,pldv: panel estimators for limited dependent
variables.Testing functions:
phtest, pFtest,
pooltest, plmtest, pwaldtest,
piest, aneweytest, mtest,
sargan),pbgtest, pwfdtest,
pbnftest, pdwtest, pwartest,
pbsytest, pbltest),pcdtest),purtest, cipstest,
phansitest),pgrangertest).Robust covariance matrix estimators (incl. various weighting schemes for small sample adjustment):
vcovHC: Arellano (1987), White (1980),vcovBK: Beck and Katz (1995) (PCSE),vcovNW: Newey and West (1987),vcovDC: double-clustering robust (Thompson (2011),
Cameron et al. (2011)),vcovSCC: Driscoll and Kraay (1998).An enhanced data frame, called pdata.frame, to deal
with data sets for which observations are identified by a combination of
two indexes.
Panel data transformation functions (e.g., Within,
Between, between, lag,
lead, diff).
Other functions relating to panel data sets, e.g.:
pdim),is.pbalanced) and
consecutiveness (regularity) (is.pconsecutive) as well as
functions to change data to conform to these properties
(make.pbalanced, make.pconsecutive),punbalancedness)
(Ahrens/Pincus (1981)).To install the released version from CRAN:
install.packages("plm")The package’s CRAN website is https://cran.r-project.org/package=plm.
The development of package plm takes place on GitHub at
https://github.com/ycroissant/plm. To install the
development version from GitHub, use, e.g.:
# install.packages("remotes") # remove '#' if pkg 'remotes' is not installed
remotes::install_github("ycroissant/plm")Package plm comes with documentation: Besides the usual help pages
for each function, the vignettes provide a gentle introduction to the
package and some functions. Vignettes are available at the package’s
CRAN website https://cran.r-project.org/package=plm and can be
browsed from within R by browseVignettes("plm").
New package users are advised to start with the first vignette Panel data econometrics in R: the plm package for an overview of the package. A more in-depth treatment of estimation of error component models and instrument variable models is in the second vignette Estimation of error component models with the plm function.
Further, many textbooks treat package plm and/or use it
in their examples:
Croissant/Millo, Panel Data Econometrics with R, 2019, John Wiley & Sons, Hoboken.
Kleiber/Zeileis, Applied Econometrics with R, 2008, Springer, New York. Esp. chapter 3.6.
Hanck/Arnold/Gerber/Schmelzer, Econometrics with R, online book https://www.econometrics-with-r.org/. Esp. chapter 10.
Heiss, Using R for Introductory Econometrics, 2nd edition, 2020, Independent Publishing, Düsseldorf, also available online at http://www.urfie.net/. A companion book using R to Wooldridge, Introductory Econometrics, esp. chapters 13-14.