pp.test
Phillips-Perron Test for Unit Roots
Description
Computes the Phillips-Perron test for the null hypothesis that x
has a unit root against a stationary alternative.
Usage
PP.test(x, lshort = TRUE)
Arguments
x | a numeric vector or univariate time series. |
lshort | a logical indicating whether the short or long version of the truncation lag parameter is used. |
Details
The general regression equation which incorporates a constant and a linear trend is used and the corrected t-statistic for a first order autoregressive coefficient equals one is computed. To estimate sigma^2
the Newey-West estimator is used. If lshort
is TRUE
, then the truncation lag parameter is set to trunc(4*(n/100)^0.25)
, otherwise trunc(12*(n/100)^0.25)
is used. The p-values are interpolated from Table 4.2, page 103 of Banerjee et al (1993).
Missing values are not handled.
Value
A list with class "htest"
containing the following components:
statistic | the value of the test statistic. |
parameter | the truncation lag parameter. |
p.value | the p-value of the test. |
method | a character string indicating what type of test was performed. |
data.name | a character string giving the name of the data. |
Author(s)
A. Trapletti
References
A. Banerjee, J. J. Dolado, J. W. Galbraith, and D. F. Hendry (1993). Cointegration, Error Correction, and the Econometric Analysis of Non-Stationary Data. Oxford University Press, Oxford.
P. Perron (1988). Trends and random walks in macroeconomic time series. Journal of Economic Dynamics and Control, 12, 297–332. doi: 10.1016/0165-1889(88)90043-7.
Examples
x <- rnorm(1000) PP.test(x) y <- cumsum(x) # has unit root PP.test(y)
Copyright (©) 1999–2012 R Foundation for Statistical Computing.
Licensed under the GNU General Public License.