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/Phillips/Schmidt/Shin (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
Choi, I. (2001). “Unit root tests for panel data”, Journal of International Money and Finance, 20(2), pp. 249–272.
Hadri K. (2000). “Testing for Stationarity in Heterogeneous Panel Data”, The Econometrics Journal, 3(2), pp. 148–161.
Hall A. (1994). “Testing for a Unit Root in Time Series with Pretest Data-Based Model Selection”, Journal of Business & Economic Statistics, 12(1), pp. 461–470.
Im K.S., Pesaran M.H. and Shin Y. (2003). “Testing for Unit Roots in Heterogeneous Panels”, Journal of Econometrics, 115(1), pp. 53–74.
Kwiatkowski D., Phillips P. C. B., Schmidt P. and Shin Y. (1992). “Testing the null 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(1–3), pp. 159–178.
Levin A., Lin C.-F. and Chu C.-S.J. (2002). “Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties”, Journal of Econometrics, 108(1), pp. 1–24.
MacKinnon, J.G. (1994). “Approximate Asymptotic Distribution Functions for Unit-Root and Cointegration Tests”, Journal of Business & Economic Statistics, 12(2), pp. 167–176.
Maddala G.S. and 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, Supplement 1, pp. 631–652.
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")