plm(formula,instruments=NULL,endog=NULL,data,effect="individual",
random.method="swar",inst.method="bvk",model=NULL,np=FALSE, ...)
## S3 method for class 'plm':
print(x,digits=5, ...)
## 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, ...)plm or plms,pdata.frame
and is mandatory,"individual", "time" or "twoways","swar", "amemiya", "walhus" and "nerlove","bvk" and "baltagi","pooling", "within",
"between", "random" and "ht" : plm
returns the model specified or, if NULL, a list containing
four models ("pooling"<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 c("plm","lm") if the argument
model is filled.
A "plm" object inherits form "lm". It has the following
additional elements :
within model only),within model only),random model only),random model only).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.
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 (Walhus 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".
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.frame.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)Run the code above in your browser using DataLab