Description Usage Arguments Details Value References Examples
Pesaran's CD or Breusch–Pagan's LM (local or global) tests for cross sectional dependence in panel models
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28  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,
...
)

x 
an object of class 
... 
further arguments to be passed on to 
data 
a 
index 
an optional numerical index, if 
model 
an optional character string indicating which type of
model to estimate; if left to 
test 
the type of test statistic to be returned. One of

w 
either 
These tests are originally meant to use the residuals of separate
estimation of one time–series regression for each crosssectional
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
crosssectional 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 biascorrected 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 biascorrection 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
.
An object of class "htest"
.
BALT:FENG:KAO:12plm
\insertRefBREU:PAGA:80plm
\insertRefPESA:04plm
\insertRefPESA:15plm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  data("Grunfeld", package = "plm")
## test on heterogeneous model (separate time series regressions)
pcdtest(inv ~ value + capital, data = Grunfeld,
index = c("firm", "year"))
## test on twoway 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)

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