| PP.test {stats} | R Documentation |
Computes the Phillips-Perron test for the null hypothesis that
x has a unit root against a stationary alternative.
PP.test(x, lshort = TRUE)
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. |
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.
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. |
A. Trapletti
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.
x <- rnorm(1000) PP.test(x) y <- cumsum(x) # has unit root PP.test(y)