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
pggls: estimation of general feasible generalized least squares models,
pmg: mean groups (MG), demeaned MG and common correlated effects
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) variance–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
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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