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)A list with class "htest" containing the following components:
the value of the test statistic.
the truncation lag parameter.
the p-value of the test.
a character string indicating what type of test was performed.
a character string giving the name of the data.
a character string describing the alternative hypothesis.
a numeric vector or univariate time series.
indicates the alternative hypothesis and must be
one of "stationary" (default) or "explosive". You can
specify just the initial letter.
indicates which variant of the test is computed and must
be one of "Z(alpha)" (default) or "Z(t_alpha)".
a logical indicating whether the short or long version of the truncation lag parameter is used.
A. Trapletti
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. 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.
adf.test
x <- rnorm(1000) # no unit-root
pp.test(x)
y <- cumsum(x) # has unit root
pp.test(y)
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