plm is a package for R which intends to make the estimation of linear panel models straightforward. plm provides functions to estimate a wide variety of models and to make (robust) inference.
For a gentle and comprehensive introduction to the package, please see the package's vignette.
The main functions to estimate models are:
plm
: panel data estimators using lm
on transformed data,
pvcm
: variable coefficients 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,
pldv
: panel estimators for limited dependent variables.
Next to the model estimation functions, the package offers several functions for statistical tests related to panel data/models.
Multiple functions for (robust) varianceâ€“covariance matrices are at hand as well.
The package also provides data sets to demonstrate functions and to
replicate some text book/paper results. Use
data(package="plm")
to view a list of available data sets in
the package.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23  data("Produc", package = "plm")
zz < plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state","year"))
summary(zz)
# replicates some results from Baltagi (2013), table 3.1
data("Grunfeld", package = "plm")
p < plm(inv ~ value + capital,
data = Grunfeld, model="pooling")
wi < plm(inv ~ value + capital,
data = Grunfeld, model="within", effect = "twoways")
swar < plm(inv ~ value + capital,
data = Grunfeld, model="random", effect = "twoways")
amemiya < plm(inv ~ value + capital,
data = Grunfeld, model = "random", random.method = "amemiya",
effect = "twoways")
walhus < plm(inv ~ value + capital,
data = Grunfeld, model = "random", random.method = "walhus",
effect = "twoways")

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