plm {plm}R Documentation

Panel Data Estimators

Description

Estimators for panel data (balanced or unbalanced)

Usage

plm(y, ...)
## S3 method for class 'formula':
plm(y,instruments=NULL,endog=NULL,data,effect="individual",
theta="swar",trinst="baltagi",model=NULL,np=FALSE,...)
## Default S3 method:
plm(y,X,W,id,time,pvar,pdim,pmodel, ...)
## S3 method for class 'plm':
print(x,digits=3, ...)
## S3 method for class 'plm':
summary(object, ...)
## S3 method for class 'plms':
print(x,digits=3, ...)
## S3 method for class 'plms':
summary(object, ...)
## S3 method for class 'summary.plm':
print(x,digits=3, ...)
## S3 method for class 'summary.plms':
print(x,digits=3, ...)

Arguments

y a symbolic description for the model to be estimated for the formula method, a numeric vector for the default method,
object,x an object of class plm or plms,
instruments a one side formula containing instrumental variables,
endog a one side formula containing endogenous variable,
data the data, must be an object of class pdata.frame and is compulsary,
effect one of "individual", "time" or "twoways" for a two way estimation,
theta method of estimation for the variance components in the random effect model, one of "swar", "amemiya", "walhus" and "nerlove",
trinst the instrumental variable transformation : one of "baltagi", "bvk", "ht",
model one of "pooling", "within", "between" and "random" or NULL : plm returns the model spectified or if NULL a list containing the fout models,
W a matrix of instrumental variables,
X a matrix of explanatory variables,
id the individual index,
time the time index,
pvar a list resulting from a call to pvarcheck,
pdim a list resulting from a call to pdimcheck,
pmodel a list containing the characteristics of the model to be estimated : model, formula, effect, theta, trinst,
np a logical value which indicates whether the nopool model has to be estimated or not,
digits digits,
... 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","amemiya","walhus" and "nerlove".

Instrumental variable 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 by instrumental variables if trinstr equal "bvk" (Balestra, P. and J. Varadharajan–Krishnakumar (1987)) or "baltagi" (Baltagi (1981)). If trinstr="ht", the Hausman and Taylor estimator is computed and only a random effect model is returned.

Value

Wheter :
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 "plm" if the argument model is filled or if trinst="ht".
A "plm" object is a list of the following elements : coefficients, df.residual, ssr, cov.unscaled and formula. It has print, summary and print.summary methods which are not unlike lm's methods.
A specific summary method is provided for objects of class "plms", which returns an objects of class summary.plms and prints a table of the coefficients of the different models and their standard errors.

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 0.1-1 Index]