plm package: linear models for panel data

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.

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

Author

Maintainer: Kevin Tappe kevin.tappe@bwi.uni-stuttgart.de

Authors:

• Yves Croissant yves.croissant@univ-reunion.fr

• Giovanni Millo giovanni.millo@deams.units.it

Other contributors:

• Ott Toomet otoomet@gmail.com [contributor]

• Christian Kleiber Christian.Kleiber@unibas.ch [contributor]

• Achim Zeileis Achim.Zeileis@R-project.org [contributor]

• Arne Henningsen arne.henningsen@googlemail.com [contributor]

• Liviu Andronic [contributor]

• Nina Schoenfelder [contributor]

Examples

data("Produc", package = "plm")
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
          data = Produc, index = c("state","year"))
summary(zz)
#> Oneway (individual) effect Within Model
#> 
#> Call:
#> plm(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, 
#>     data = Produc, index = c("state", "year"))
#> 
#> Balanced Panel: n = 48, T = 17, N = 816
#> 
#> Residuals:
#>      Min.   1st Qu.    Median   3rd Qu.      Max. 
#> -0.120456 -0.023741 -0.002041  0.018144  0.174718 
#> 
#> Coefficients:
#>              Estimate  Std. Error t-value  Pr(>|t|)    
#> log(pcap) -0.02614965  0.02900158 -0.9017    0.3675    
#> log(pc)    0.29200693  0.02511967 11.6246 < 2.2e-16 ***
#> log(emp)   0.76815947  0.03009174 25.5273 < 2.2e-16 ***
#> unemp     -0.00529774  0.00098873 -5.3582 1.114e-07 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Total Sum of Squares:    18.941
#> Residual Sum of Squares: 1.1112
#> R-Squared:      0.94134
#> Adj. R-Squared: 0.93742
#> F-statistic: 3064.81 on 4 and 764 DF, p-value: < 2.22e-16

# 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")