plm (version 2.1-0)

plm-package: plm package: linear models for panel data

Description

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.

Details

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, -pgmm generalized method of moments (GMM) estimation for panel data,

• pvcm variable coefficients models

• pmg mean groups (MG), demeaned MG and common correlated effects (CCEMG) estimators.

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 the package.

Examples

# NOT RUN {
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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