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", "kinla"),
    random.dfcor = NULL,
    inst.method = c("bvk", "baltagi", "am", "bmc"), restrict.matrix = NULL,
    restrict.rhs = NULL, index = NULL, ...)
## S3 method for class 'panelmodel'
print(x,digits=max(3, getOption("digits") - 2), 
  width = getOption("width"), ...)
## S3 method for class 'plm'
plot(x, dx = 0.2, N = NULL, seed = 1,
    within = TRUE, pooling = TRUE, between = FALSE, random = FALSE, ...)

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–differences ("fd"), and between ("between"). It supports unbalanced panels and two–way effects (although not with all methods).

For random effects models, four estimators of the transformation parameter are available by setting random.method to one of "swar" (Swamy and Arora (1972)) (default), "amemiya" (Amemiya (1971)), "walhus" (Wallace and Hussain (1969)), or "nerlove" (Nerlove (1971)).

For first–difference models, the intercept is maintained (which from a specification viewpoint amounts to allowing for a trend in the levels model). The user can exclude it from the estimated specification the usual way by adding "-1" to the model formula.

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", respectively.

The Hausman–Taylor estimator is computed if model = "ht".

Value

An object of class c("plm","panelmodel").

A "plm" object has the following elements :

It has print, summary and print.summary methods. The summary method creates an object of class "summary.plm" that extends the object it is run on with information about (inter alia) F statistic and (adjusted) R-squared of model, standard errors, t–values, and p–values of coefficients, (if supplied) the furnished vcov, see summary.plm for further details.

Author(s)

Yves Croissant

References

Amemiya, T. (1971) The estimation of the variances in a variance–components model, International Economic Review, 12(1), 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(2), pp. 223–246.

Baltagi, B.H. (1981) Simultaneous equations with error components, Journal of Econometrics, 17(2), pp. 189–200.

Baltagi, B.H. (2001) Econometric Analysis of Panel Data, 2nd ed., John Wiley and Sons.

Baltagi, B.H. (2013) Econometric Analysis of Panel Data, 5th ed., John Wiley and Sons.

Hausman, J.A. and Taylor W.E. (1981) Panel data and unobservable individual effects, Econometrica, 49(6), pp. 1377–1398.

Nerlove, M. (1971) Further evidence on the estimation of dynamic economic relations from a time–series of cross–sections, Econometrica, 39(2), 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(2), 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.

See Also

summary.plm for further details about the associated summary method and the "summary.plm" object both of which provide some model tests and tests of coefficients. fixef to compute the fixed effects for "within" models (=fixed effects models).

Examples

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

# replicates some results from Baltagi (2013), table 3.1
data("Grunfeld", package = "plm")
p <- plm(inv ~ value + capital,
         data = Grunfeld, model = "pooling")

wi <- plm(inv ~ value + capital,
          data = Grunfeld, model = "within", effect = "twoways")

swar <- plm(inv ~ value + capital,
            data = Grunfeld, model = "random", effect = "twoways")
          
amemiya <- plm(inv ~ value + capital,
               data = Grunfeld, model = "random", random.method = "amemiya",
               effect = "twoways")
                
walhus <- plm(inv ~ value + capital,
              data = Grunfeld, model = "random", random.method = "walhus",
              effect = "twoways")

# summary, summary with a funished vcov, passed as matrix, 
# as function, and as function with additional argument
summary(wi)
summary(wi, vcov = vcovHC(wi))
summary(wi, vcov = vcovHC)
summary(wi, vcov = function(x) vcovHC(x, method = "white2"))