plm {plm} | R Documentation |
Linear models for panel data estimated using the lm
function to
transformed data.
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, ...)
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. |
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"
.
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.
Yves Croissant
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)