purtest  R Documentation 
purtest
implements several testing procedures that have been proposed
to test unit root hypotheses with panel data.
purtest( object, data = NULL, index = NULL, test = c("levinlin", "ips", "madwu", "Pm", "invnormal", "logit", "hadri"), exo = c("none", "intercept", "trend"), lags = c("SIC", "AIC", "Hall"), pmax = 10, Hcons = TRUE, q = NULL, dfcor = FALSE, fixedT = TRUE, ips.stat = NULL, ... ) ## S3 method for class 'purtest' print(x, ...) ## S3 method for class 'purtest' summary(object, ...) ## S3 method for class 'summary.purtest' print(x, ...)
object, x 
Either a 
data 
a 
index 
the indexes, 
test 
the test to be computed: one of 
exo 
the exogenous variables to introduce in the augmented
Dickeyâ€“Fuller (ADF) regressions, one of: no exogenous
variables ( 
lags 
the number of lags to be used for the augmented
DickeyFuller regressions: either a single value integer (the number of
lags for all time series), a vector of integers (one for each
time series), or a character string for an automatic
computation of the number of lags, based on the AIC
( 
pmax 
maximum number of lags (irrelevant for 
Hcons 
logical, only relevant for 
q 
the bandwidth for the estimation of the longrun variance
(only relevant for 
dfcor 
logical, indicating whether the standard deviation of the regressions is to be computed using a degreesoffreedom correction, 
fixedT 
logical, indicating whether the individual ADF
regressions are to be computed using the same number of
observations (irrelevant for 
ips.stat 

... 
further arguments (can set argument 
All these tests except "hadri"
are based on the estimation of
augmented DickeyFuller (ADF) regressions for each time series. A
statistic is then computed using the tstatistics associated with
the lagged variable. The Hadri residualbased LM statistic is the
crosssectional average of the individual KPSS statistics
\insertCiteKWIA:PHIL:SCHM:SHIN:92;textualplm, standardized by their
asymptotic mean and standard deviation.
Several Fishertype tests that combine pvalues from tests based on ADF regressions per individual are available:
"madwu"
is the inverse chisquared test
\insertCiteMADDA:WU:99;textualplm, also called P test by
\insertCiteCHOI:01;textualplm.
"Pm"
is the modified P test proposed by
\insertCiteCHOI:01;textualplm for large N,
"invnormal"
is the inverse normal test by \insertCiteCHOI:01;textualplm, and
"logit"
is the logit test by \insertCiteCHOI:01;textualplm.
The individual pvalues for the Fishertype tests are approximated as described in \insertCiteMACK:96;textualplm if the package urca (\insertCitePFAFF:08;textualplm) is available, otherwise as described in \insertCiteMACK:94;textualplm.
For the test statistic tbar of the test of Im/Pesaran/Shin (2003)
(ips.stat = "tbar"
), no pvalue is given but 1%, 5%, and 10% critical
values are interpolated from paper's tabulated values via inverse distance
weighting (printed and contained in the returned value's element
statistic$ips.tbar.crit
).
Hadri's test, the test of Levin/Lin/Chu, and the tbar statistic of
Im/Pesaran/Shin are not applicable to unbalanced panels; the tbar statistic
is not applicable when lags > 0
is given.
The exogenous instruments of the tests (where applicable) can be specified in several ways, depending on how the data is handed over to the function:
For the formula
/data
interface (if data
is a data.frame
,
an additional index
argument should be specified); the formula
should be of the form: y ~ 0
, y ~ 1
, or y ~ trend
for a test
with no exogenous variables, with an intercept, or with individual
intercepts and time trend, respectively. The exo
argument is
ignored in this case.
For the data.frame
, matrix
, and pseries
interfaces: in
these cases, the exogenous variables are specified using the exo
argument.
With the associated summary
and print
methods, additional
information can be extracted/displayed (see also Value).
For purtest: An object of class "purtest"
: a list with the elements
named:
"statistic"
(a "htest"
object),
"call"
,
"args"
,
"idres"
(containing results from the individual regressions),
"adjval"
(containing the simulated means and variances needed to compute
the statistic, for test = "levinlin"
and "ips"
, otherwise NULL
),
"sigma2"
(shortrun and longrun variance for test = "levinlin"
,
otherwise NULL
).
Yves Croissant and for "Pm", "invnormal", and "logit" Kevin Tappe
cipstest()
, phansitest()
data("Grunfeld", package = "plm") y < data.frame(split(Grunfeld$inv, Grunfeld$firm)) # individuals in columns purtest(y, pmax = 4, exo = "intercept", test = "madwu") ## same via pseries interface pGrunfeld < pdata.frame(Grunfeld, index = c("firm", "year")) purtest(pGrunfeld$inv, pmax = 4, exo = "intercept", test = "madwu") ## same via formula interface purtest(inv ~ 1, data = Grunfeld, index = c("firm", "year"), pmax = 4, test = "madwu")
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