| PP.test {stats} | R Documentation |
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, Dolado, Galbraith, and Hendry (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
Banerjee A, Dolado JJ, Galbraith JW, Hendry D (1993). Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data. Oxford University Press. ISBN 9780198288107. doi:10.1093/0198288107.001.0001.
Perron P (1988). “Trends and Random Walks in Macroeconomic Time Series.” Journal of Economic Dynamics and Control, 12(2–3), 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)