spdep (version 0.4-9)

bptest.sarlm: Breusch-Pagan test for spatial models

Description

Performs the Breusch-Pagan test for heteroskedasticity on the least squares fit of the spatial models taking the spatial coefficients rho or lambda into account. This function is a copy of the bptest function in package "lmtest", modified to use objects returned by spatial simultaneous autoregressive models.

Usage

bptest.sarlm(object, varformula=NULL, studentize = TRUE, data=list())

Arguments

object

An object of class "sarlm" from errorsarlm() or lagsarlm().

varformula

a formula describing only the potential explanatory variables for the variance (no dependent variable needed). By default the same explanatory variables are taken as in the main regression model

studentize

logical. If set to TRUE Koenker's studentized version of the test statistic will be used.

data

an optional data frame containing the variables in the varformula

Value

• A list with class "htest" containing the following components:
• statistic
  the value of the test statistic.
• p.value
  the p-value of the test.
• parameter
  degrees of freedom.
• method
  a character string indicating what type of test was performed.

encoding

latin1

Details

Asymptotically this corresponds to the test given by Anselin (1988), but is not exactly the same. The studentized version is more conservative and perhaps to be prefered. The residuals, and for spatial error models the RHS variables, are adjusted for the spatial coefficient, as suggested bt Luc Anselin (personal communication).

It is also technically possible to make heteroskedasticity corrections to standard error estimates by using the lm.target component of sarlm objects - using functions in the lmtest and sandwich packages.

References

T.S. Breusch & A.R. Pagan (1979), A Simple Test for Heteroscedasticity and Random Coefficient Variation. Econometrica 47, 1287--1294

W. Kr�mer & H. Sonnberger (1986), The Linear Regression Model under Test. Heidelberg: Physica.

L. Anselin (1988) Spatial econometrics: methods and models. Dordrecht: Kluwer, pp. 121--122.

See Also

errorsarlm, lagsarlm

Examples

example(columbus)
error.col <- errorsarlm(CRIME ~ HOVAL + INC, data=columbus,
 nb2listw(col.gal.nb))
bptest.sarlm(error.col)
bptest.sarlm(error.col, studentize=FALSE)
if (require(lmtest) && require(sandwich)) {
  coeftest(error.col$lm.target, vcov=vcovHC(error.col$lm.target,
    type="HC0"), df=Inf)
}