pcdtest: Tests of cross-section dependence for panel models

In ycroissant/plm: Linear Models for Panel Data

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 'formula'
pcdtest(
  x,
  data,
  index = NULL,
  model = NULL,
  test = c("cd", "sclm", "bcsclm", "lm", "rho", "absrho"),
  w = NULL,
  ...
)

## S3 method for class 'panelmodel'
pcdtest(
  x,
  test = c("cd", "sclm", "bcsclm", "lm", "rho", "absrho"),
  w = NULL,
  ...
)

## S3 method for class 'pseries'
pcdtest(
  x,
  test = c("cd", "sclm", "bcsclm", "lm", "rho", "absrho"),
  w = NULL,
  ...
)

Arguments

x	an object of class formula, panelmodel, or pseries (depending on the respective interface) describing the model to be tested,
...	further arguments to be passed on for model estimation to plm, such as effect or random.method.
data	a data.frame,
index	an optional numerical index, if NULL, the first two columns of the data.frame provided in argument data are assumed to be the index variables; for further details see pdata.frame(),
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, "bcsclm" for the bias-corrected scaled version of Breusch and Pagan's LM statistic, "rho" for the average correlation coefficient, "absrho" for the average absolute correlation coefficient,
w	either NULL (default) for the global tests or – for the local versions of the statistics – a ⁠n x n⁠ matrix describing proximity between individuals, 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)==FALSE elsewhere. Only the lower triangular part (without diagonal) of w after coercing by as.logical() is evaluated for neighbouring information (but w can be symmetric). See also Details and Examples,

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 (model = NULL). If a different model specification (model = "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 ⁠data frame⁠; 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 (except for test = "bcsclm" which requires a within model with either individual or two-ways 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 by supplying a proximity matrix (elements coercible to logical) with argument w which provides information on whether any pair of individuals 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 need 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 spdep's function nb2mat before using.

The methods implemented are suitable also for unbalanced panels.

Pesaran's CD test (test="cd"), Breusch and Pagan's LM test (test="lm"), and its scaled version (test="sclm") are all described in \insertCitePESA:04;textualplm (and complemented by Pesaran (2005)). The bias-corrected scaled test (test="bcsclm") is due to \insertCiteBALT:FENG:KAO:12plm and only valid for within models including the individual effect (it's unbalanced version uses max(Tij) for T) in the bias-correction term). \insertCiteBREU:PAGA:80;textualplm is the original source for the LM test.

The test on a pseries is the same as a test on a pooled regression model of that variable on a constant, i.e., pcdtest(some_pseries) is equivalent to ⁠pcdtest(plm(some_var ~ 1, data = some_pdata.frame, model = "pooling")⁠ and also equivalent to pcdtest(some_var ~ 1, data = some_data), where some_var is the variable name in the data which corresponds to some_pseries.

Value

An object of class "htest".

References

\insertRef

BALT:FENG:KAO:12plm

\insertRef

BREU:PAGA:80plm

\insertRef

PESA:04plm

\insertRef

PESA:15plm

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 two-way fixed effects homogeneous model
pcdtest(inv ~ value + capital, data = Grunfeld, model = "within",
        effect = "twoways", index = c("firm", "year"))

## test on panelmodel object
g <- plm(inv ~ value + capital, data = Grunfeld, index = c("firm", "year"))
pcdtest(g)

## scaled LM test
pcdtest(g, test = "sclm")

## test on pseries
pGrunfeld <- pdata.frame(Grunfeld)
pcdtest(pGrunfeld$value)

## local test
## define neighbours for individual 2: 1, 3, 4, 5 in lower triangular matrix
w <- matrix(0, ncol= 10, nrow=10)
w[2,1] <- w[3,2] <- w[4,2] <- w[5,2] <- 1
pcdtest(g, w = w)

ycroissant/plm documentation built on July 8, 2024, 3:59 a.m.