| plm {plm} | R Documentation |
Linear models for panel data estimated using the lm function on transformed data.
plm(formula, data, subset, na.action, effect = c("individual","time","twoways"),
model = c("within","random","ht","between","pooling","fd"),
random.method = c("swar","walhus","amemiya","nerlove"),
inst.method = c("bvk","baltagi"), index = NULL, ...)
## S3 method for class 'plm'
summary(object, ...)
## S3 method for class 'summary.plm'
print(x, digits = max(3, getOption("digits") - 2),
width = getOption("width"), ...)
formula |
a symbolic description for the model to be estimated, |
object,x |
an object of class |
data |
a |
subset |
see |
na.action |
see |
effect |
the effects introduced in the model, one of
|
model |
one of |
random.method |
method of estimation for the variance components in the random effects model, one of |
inst.method |
the instrumental variable transformation: one of |
index |
the indexes, |
digits |
digits, |
width |
the maximum length of the lines in the printed output, |
... |
further arguments. |
plm is a general function for the estimation of linear
panel models. It supports the following estimation methods:
pooled OLS (model="pooling"), fixed effects ("within"), random effects ("random"), first–differences ("fd") and between ("between"). It supports unbalanced panels and two–way effects (although not with all methods).
For random effects models, 4 estimators of the transformation
parameter are available : swar (Swamy and Arora),
amemiya, walhus (Wallace and Hussain) and nerlove.
Instrumental variables estimation is obtained using two-part formulas, the second part indicating the instrumental variables used. This can be a complete list of instrumental variables or an update of the first part. If, for example, the model is y ~ x1 + x2 + x3, with x1 and x2 endogenous and z1 and z2 external instruments, the model can be estimated with:
formula=y~x1+x2+x3 | x3+z1+z2,
formula=y~x1+x2+x3 | .-x1-x2+z1+z2.
Balestra and Varadharajan–Krishnakumar's or Baltagi's method is used if inst.method="bvk" or if inst.method="baltagi".
The Hausman and Taylor estimator is computed if model="ht".
An object of class c("plm","panelmodel").
A "plm" object has the following elements :
coefficients |
the vector of coefficients, |
vcov |
the covariance matrix of the coefficients, |
residuals |
the vector of residuals, |
df.residual |
degrees of freedom of the residuals, |
formula |
an object of class |
model |
a data.frame of class |
ercomp |
an object of class |
call |
the call, |
It has print, summary and print.summary methods.
Yves Croissant
Amemiya, T. (1971) The estimation of the variances in a variance–components model, International Economic Review, 12, pp.1–13.
Balestra, P. and Varadharajan–Krishnakumar, J. (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, 2nd ed. John Wiley and Sons, Ltd.
Hausman, J.A. and Taylor W.E. (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 Arora, S.S. (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 Hussain, A. (1969) The use of error components models in combining cross section with time series data, Econometrica, 37(1), pp.55–72.
data("Produc", package = "plm")
zz <- plm(log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp, data = Produc, index = c("state","year"))
summary(zz)