purtest {plm} | 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, ...) ## 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
Dickey–Fuller (ADF) regressions, one of: no exogenous
variables ( |
lags |
the number of lags to be used for the augmented
Dickey-Fuller regressions: either an 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 either the AIC
( |
pmax |
maximum number of lags, |
Hcons |
logical, only relevant for |
q |
the bandwidth for the estimation of the long-run variance, |
dfcor |
logical, indicating whether the standard deviation of the regressions is to be computed using a degrees-of-freedom correction, |
fixedT |
logical, indicating whether the different ADF regressions are to be computed using the same number of observations, |
... |
further arguments. |
All these tests except "hadri"
are based on the estimation of
augmented Dickey-Fuller (ADF) regressions for each time series. A
statistic is then computed using the t-statistics associated with
the lagged variable. The Hadri residual-based LM statistic is the
cross-sectional average of the individual KPSS statistics
(Kwiatkowski et al. 1992), standardized by their
asymptotic mean and standard deviation.
Several Fisher-type tests that combine p-values from tests based on ADF regressions per individual are available:
"madwu"
is the inverse chi-squared test
(Maddala and Wu 1999), also called P test by
Choi (2001).
"Pm"
is the modified P test proposed by
Choi (2001) for large N,
"invnormal"
is the inverse normal test by (Choi 2001), and
"logit"
is the logit test by (Choi 2001).
The individual p-values for the Fisher-type tests are approximated as described in MacKinnon (1994).
The kind of test to be computed 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).
An object of class "purtest"
: a list with the elements
"statistic"
(a "htest"
object), "call"
, "args"
,
"idres"
(containing results from the individual regressions),
and "adjval"
(containing the simulated means and variances
needed to compute the statistic).
Yves Croissant and for "Pm", "invnormal", and "logit" Kevin Tappe
Choi I (2001).
“Unit root tests for panel data.”
Journal of International Money and Finance, 20(2), 249 - 272.
ISSN 0261-5606, http://www.sciencedirect.com/science/article/pii/S0261560600000486.
Hadri K (2000).
“Testing for stationarity in heterogeneous panel data.”
The Econometrics Journal, 3(2), 148–161.
ISSN 13684221, 1368423X.
Hall A (1994).
“Testing for a unit root in time series with pretest data-based model selection.”
Journal of Business \& Economic Statistics, 12(4), 461–470.
Im K, Pesaran M, Shin Y (2003).
“Testing for unit roots in heterogenous panels.”
Journal of Econometrics, 115(1), 53-74.
Kwiatkowski D, Phillips PC, Schmidt P, Shin Y (1992).
“Testing the null hypothesis of stationarity against the
alternative of a unit root: How sure are we that
economic time series have a unit root?”
Journal of Econometrics, 54(1), 159 - 178.
ISSN 0304-4076, http://www.sciencedirect.com/science/article/pii/030440769290104Y.
Levin A, Lin C, Chu C (2002).
“Unit root test in panel data : asymptotic and finite sample properties.”
Journal of Econometrics, 108, 1-24.
MacKinnon JG (1994).
“Approximate Asymptotic Distribution Functions for Unit-Root and Cointegration Tests.”
Journal of Business & Economic Statistics, 12(2), 167–176.
ISSN 07350015.
Maddala G, Wu S (1999).
“A comparative study of unit root tests with panel data and a new simple test.”
Oxford Bulletin of Economics and Statistics, 61, 631-52.
data("Grunfeld", package = "plm") y <- data.frame(split(Grunfeld$inv, Grunfeld$firm)) purtest(y, pmax = 4, exo = "intercept", test = "madwu") ## same via formula interface purtest(inv ~ 1, data = Grunfeld, index = c("firm", "year"), pmax = 4, test = "madwu")