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 = "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", "fd" and "ht",

instruments

a one--sided 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.

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 (code{"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 : code{swar} (Swamy and Arora),
  code{amemiya}, code{walhus} (Wallace and Hussain) and code{nerlove}.

  Instrumental variables estimation is obtained using different
  syntaxes. If for example, the model is code{y~x1+x2+x3}, code{x1},
  code{x2} are endogenous and code{z1}, code{z2} are external
  instruments, the model can be estimated with :

  itemize{
    item code{formula=y~x1+x2+x3, instruments=~x3+z1+z2},
    item code{formula=y~x1+x2+x3, instruments=~.-x1-x2+z1+z2},
    item code{formula=y~x1+x2+x3 | x3+z1+z2},
    item code{formula=y~x1+x2+x3 | .-x1-x2+z1+z2}.
  }

  Balestra and Varadharajan--Krishnakumar's or Baltagi's method is used if
  code{inst.method="bvk"} or if  code{inst.method="baltagi"}.
  
  The Hausman and Taylor estimator is computed if code{model="ht"}.
  
}

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.


author{Yves Croissant}

references{
  Amemiyia, T. (1971) The estimation of the variances in a
  variance--components model, emph{International Economic Review}, bold{12},
  pp.1--13.

  Balestra, P. and Varadharajan--Krishnakumar, J. (1987) Full
  information estimations of a system of simultaneous equations with
  error components structure, emph{Econometric Theory}, bold{3}, pp.223--246.
  
  Baltagi, B.H. (1981) Simultaneous equations with error components,
  emph{Journal of econometrics}, bold{17}, pp.21--49.
  
  Baltagi, B.H. (2001) emph{Econometric Analysis of Panel Data}. John
  Wiley and sons. ltd.

  Hausman, J.A. and  Taylor W.E. (1981) Panel data and unobservable
  individual effects, emph{Econometrica}, bold{49}, pp.1377--1398.
  
  Nerlove, M. (1971) Further evidence on the estimation of dynamic
  economic relations from a time--series of cross--sections,
  emph{Econometrica}, bold{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, emph{Econometrica}, bold{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,
  emph{Econometrica}, bold{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)
}
keyword{regression}