plm (version 1.2-0)

pcdtest: Tests of cross-section dependence for panel models

Description

Pesaran's CD or Breusch-Pagan's LM (local or global) tests for cross sectional dependence in panel models

Usage

pcdtest(x, ...)
## S3 method for class 'panelmodel':
pcdtest(x, test=c("cd","sclm","lm"), w=NULL, ...)
## S3 method for class 'formula':
pcdtest(x, data, index=NULL, model=NULL, test=c("cd","sclm","lm"), w=NULL, ...)

Arguments

x

an object of class formula or panelmodel, describing the model to be tested

data

a data.frame

index

an optional numerical index, in case data has to be formatted by plm.data

model

an optional character string indicating which type of model to estimate; if left to NULL, the original heterogeneous specification of Pesaran is used

test

the type of test statistic to be returned. One of "cd" for Pesaran's CD statistic, "lm" for Breusch and Pagan's original LM statistic, "sclm" for the scaled version of Breusch and Pagan's LM statistic

w

an $nxn$ matrix describing proximity between observations, with $w_ij=a$ where $a$ is any number such that as.logical(a)=TRUE, if $i,j$ are neighbours, $0$ or any number $b$ such that as.logical(b)=F

...

further arguments to be passed on to plm, such as e.g. effect or random.method

Value

• An object of class "htest".

Details

These tests are originally meant to use the residuals of separate estimation of one time-series regression for each cross-sectional unit in order to check for cross-sectional dependence. If a different model specification (within, random, ...) is assumed consistent, one can resort to its residuals for testing (which is common, e.g., when the time dimension's length is insufficient for estimating the heterogeneous model). If the time dimension is insufficient and model=NULL, the function defaults to estimation of a within model and issues a warning. The main argument of this function may be either a model of class panelmodel or a formula and dataframe; in the second case, unless model is set to NULL, all usual parameters relative to the estimation of a plm model may be passed on. The test is compatible with any consistent panelmodel for the data at hand, with any specification of effect. E.g., specifying effect=''time'' or effect=''twoways'' allows to test for residual cross-sectional dependence after the introduction of time fixed effects to account for common shocks. A local version of either test can be computed supplying a proximity matrix (coercible to logical) providing information on whether any pair of observations are neighbours or not. If w is supplied, only neighbouring pairs will be used in computing the test; else, w will default to NULL and all observations will be used. The matrix needs not be binary, so commonly used ``row-standardized'' matrices can be employed as well. nb objects from spdep must instead be transformed into matrices by nb2mat before using.

References

Breusch, T.S. and A.R. Pagan (1980), The Lagrange multiplier test and its applications to model specification in econometrics, Review of Economic Studies, 47, pp.239--253. Pesaran, H. (2004), Tests for cross-sectional dependence in panels, CESIfo Working Paper, .., pp.--.

Examples

data(Grunfeld, package="plm")
## test on heterogeneous model (separate time series regressions)
pcdtest(inv ~ value + capital, data=Grunfeld,
        index = c("firm","year"))
## test on twoways fixed effects homogeneous model
pcdtest(inv ~ value + capital, data=Grunfeld, model="within",
        effect="twoways", , index = c("firm","year"))
## test on model object
g <- plm(inv ~ value + capital, data=Grunfeld, index = c("firm","year"))
pcdtest(g)
## scaled LM test
pcdtest(g,test="sclm")