plm (version 1.2-0)

plm: Panel Data Estimators

Description

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

Usage

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"), ...)

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", "fd" and "ht",

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,

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,
• 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 'pFormula' descrbing the model,
• model
  a data.frame of class 'pdata.frame' containing the variables used for the estimation: the response is in first position and the two indexes in the last positions,
• ercomp
  an object of class 'ercomp' providing the estimation of the components of the errors (for random effect models only),
• call
  the call,
• It has print, summary and print.summary methods.

Details

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--difference ("fd") and between ("between"). It supports unbalanced panels and two--ways effects (although not with all methods). 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 two-parts formula, 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, x1, x2 are endogenous and z1, z2 are 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".

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