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 et al (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.

A. Trapletti

### 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. doi:10.1016/0165-1889(88)90043-7.

### Examples

x <- rnorm(1000)
PP.test(x)
y <- cumsum(x) # has unit root
PP.test(y)


[Package stats version 4.2.0 Index]