plm {plm} | R Documentation |
Estimators for panel data (balanced or unbalanced)
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, ...)
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. |
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.
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.
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.
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)