plm(y, ...)
## S3 method for class 'formula':
plm(y,instruments=NULL,endog=NULL,data,effect="individual",
theta="swar",trinst="baltagi",model=NULL,np=FALSE,...)
## S3 method for class 'default':
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, ...)formula method, a numeric vector for the default method,plm or plms,pdata.frame
and is compulsary,"individual", "time" or "twoways" for a two way estimation,"swar", "amemiya", "walhus" and "nerlove","baltagi", "bvk", "ht","pooling", "within",
"between" and "random" or NULL : plm
returns the model spectified or if NULLa list containing the
fout models,pvarcheck,pdimcheck,model, formula, effect, theta,
trinst,nopool
model has to be estimated or not,"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.
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.
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.
pdata.frame for the creation of a pdata.framelibrary(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)Run the code above in your browser using DataLab