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)