plm (version 0.3-1)

plm: Panel Data Estimators

Description

Linear models for panel data estimated using the lm function to transformed data.

Usage

plm(formula, data, subset, na.action, effect = "individual",
    model = "within", instruments = NULL, random.method = "swar",
    inst.method = "bvk", index = NULL, pvar = TRUE, ...)
## S3 method for class 'plm':
summary(object, ...)
## S3 method for class 'summary.plm':
print(x, digits = max(3, getOption("digits") - 2),
    width = getOption("width"), ...)

Arguments

formula

a symbolic description for the model to be estimated,

object,x

an object of class "plm",

data

a data.frame,

subset

see lm,

na.action

see lm,

effect

the effects introduced in the model, one of "individual", "time" or "twoways",

model

one of "pooling", "within", "between", "random", and "ht",

instruments

a one side formula containing instrumental 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",

index

the indexes,

pvar

if TRUE, the pvar function is called,

digits

digits,

width

the maximum length of the lines in the print output,

...

further arguments.

Value

• an object of class c("plm","panelmodel").

  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),
• indexes
  a list containing the two index vectors (id and time).
• 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.

Details

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 (Wallace and Hussain) and nerlove.

Instrumental variables estimation is obtained using different syntaxes. 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 :

• formula=y~x1+x2+x3, instruments=~x3+z1+z2,
• formula=y~x1+x2+x3, instruments=~.-x1-x2+z1+z2,
• 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".

References

Amemiyia, 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. 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.

Examples

data("Produc", package="Ecdat")
zz <- plm(log(gsp)~log(pcap)+log(pc)+log(emp)+unemp, data=Produc, index=c("state","year"))
summary(zz)