Phillips–Perron Unit Root Test
Computes the Phillips-Perron test for the null hypothesis that
x has a unit root.
pp.test(x, alternative = c("stationary", "explosive"),
type = c("Z(alpha)", "Z(t_alpha)"), lshort = TRUE)x |
a numeric vector or univariate time series. |
alternative |
indicates the alternative hypothesis and must be
one of |
type |
indicates which variant of the test is computed and must
be one of |
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 Z(alpha) or Z(t_alpha)
statistic for a first order autoregressive coefficient equals one are
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 4.1 and 4.2, p. 103 of Banerjee et al. (1993). If the
computed statistic is outside the table of critical values, then a
warning message is generated.
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. |
alternative |
a character string describing the alternative hypothesis. |
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) # no unit-root pp.test(x) y <- cumsum(x) # has unit root pp.test(y)
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