plm {plm}R Documentation

Panel Data Estimators

Description

Linear models for panel data estimated using the lm function to transformed data.

Usage

plm(formula,data,effect="individual",model=NULL,instruments=NULL,endog=NULL,
random.method="swar",inst.method="bvk", ...)
## S3 method for class 'plm':
summary(object, ...)
## S3 method for class 'plms':
print(x,digits=5, ...)
## S3 method for class 'plms':
summary(object, ...)
## S3 method for class 'summary.plm':
print(x,digits=5,length.line=70, ...)
## S3 method for class 'summary.plms':
print(x,digits=5,length.line=70, ...)

Arguments

formula a symbolic description for the model to be estimated,
object,x an object of class plm or plms,
data the data, must be an object of class pdata.frame and is mandatory,
effect the effects introduced in the model, one of "individual", "time" or "twoways",
model one of "pooling", "within", "between", "random", "nopool" and "ht" : plm returns the model specified or, if NULL, a list containing four models ("pooling", "within", "between" and "random"),
instruments a one side formula containing instrumental variables,
endog a one side formula containing endogenous variables,
random.method method of estimation for the variance components in the random effect model, one of "swar" (the default value), "amemiya", "walhus" and "nerlove",
inst.method the instrumental variable transformation : one of "bvk" and "baltagi",
digits digits,
length.line the maximum length of the lines in the print output,
... further arguments.

Details

plm is a general function for the estimation of linear panel models. It offers limited support for unbalanced panels and estimation of two-ways effects models.

For random effect models, 4 estimators of the transformation parameter are available : swar (Swamy and Arora), amemiya, walhus (Wallace and Hussain) and nerlove.

Instrumental variables estimation is obtained using the instruments and/or endog arguments. If for example, the model is y~x1+x2+x3, x1, x2 are endogenous and z1, z2 are external instruments, the model can be estimated with : instruments=~x3+z1+z2, or instruments=~z1+z2,endog=~x1+x2. The four models are estimated using Balestra and Varadharajan–Krishnakumar's method if inst.method=bvk or Baltagi's method if inst.method="baltagi".

The Hausman and Taylor estimator is computed if model="ht".

Value

Whether : an object of class "plms", which is a list of the following models : pooling, between (between.id and between.time if method="twoways"), within and random which are all of class "plm",
an object of class c("plm","panelmodel") if the argument model is filled.
A "plm" object has the following elements :

coefficients the vector of coefficients,
residuals the vector of residuals,
fitted.values the vector of fitted.values,
vcov the covariance matrix of the coefficients,
df.residual degrees of freedom of the residuals,
model a data.frame containing the variables used for the estimation,
call the call,
FE the fixed effects (only for within models),
alpha the overall intercept (only for within models),
theta the parameter of transformation (only for random effect models),
sigma2 the variance of the different elements of the error (only for random effect models),


It has print, summary and print.summary methods.
A specific summary method is provided for objects of class "plms", which returns an object of class summary.plms and prints a table of the coefficients of the within and random models and their standard errors.

Author(s)

Yves Croissant

References

Amemiyia, T. (1971), The estimation of the variances in a variance–components model, International Economic Review, 12, pp.1–13.

Balestra, P. and J. Varadharajan–Krishnakumar (1987), Full information estimations of a system of simultaneous equations with error components structure, Econometric Theory, 3, pp.223–246.

Baltagi, B.H. (1981), Simultaneous equations with error components, Journal of econometrics, 17, pp.21–49.

Baltagi, B.H. (2001) Econometric Analysis of Panel Data. John Wiley and sons. ltd.

Hausman, J.A. and W.E. Taylor (1981), Panel data and unobservable individual effects, Econometrica, 49, pp.1377–1398.

Nerlove, M. (1971), Further evidence on the estimation of dynamic economic relations from a time–series of cross–sections, Econometrica, 39, pp.359–382.

Swamy, P.A.V.B. and S.S. Arora (1972), The exact finite sample properties of the estimators of coefficients in the error components regression models, Econometrica, 40, pp.261–275.

Wallace, T.D. and A. Hussain (1969), The use of error components models in combining cross section with time series data, Econometrica, 37(1), pp.55–72.

See Also

pdata.frame for the creation of a pdata.frame.

Examples

library(Ecdat)
data(Produc)
Produc <-pdata.frame(Produc,"state","year")
zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp,data=Produc)
summary(zz$random)

[Package plm version 0.2-2 Index]