plm-package {plm}

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,

• 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) 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.

Author(s)

Maintainer: Kevin Tappe [e-mail]

Authors:

• Yves Croissant [e-mail]

• Giovanni Millo [e-mail]

Other contributors:

• Ott Toomet [e-mail] [contributor]

• Christian Kleiber [e-mail] [contributor]

• Achim Zeileis [e-mail] [contributor]

• Arne Henningsen [e-mail] [contributor]

• Liviu Andronic [contributor]

• Nina Schoenfelder [contributor]

See Also

Useful links:

• https://cran.r-project.org/package=plm

• https://github.com/ycroissant/plm

• Report bugs at https://github.com/ycroissant/plm/issues

Examples

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")