# PP.test

##### Phillips-Perron Test for Unit Roots

Computes the Phillips-Perron test for the null hypothesis that
`x`

has a unit root against a stationary alternative.

- Keywords
- ts

##### 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.

##### 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.

##### Examples

`library(stats)`

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

*Documentation reproduced from package stats, version 3.3, License: Part of R 3.3*