stats (version 3.6.1)

PP.test: 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.

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.

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.

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. 10.1016/0165-1889(88)90043-7.

Examples

# NOT RUN {
x <- rnorm(1000)
PP.test(x)
y <- cumsum(x) # has unit root
PP.test(y)
# }