pp.test {tseries} | R Documentation |

## Phillips-Perron Unit Root Test

### Description

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

has a unit root.

### Usage

pp.test(x, alternative = c("stationary", "explosive"),
type = c("Z(alpha)", "Z(t_alpha)"), lshort = TRUE)

### Arguments

`x` |
a numeric vector or univariate time series. |

`alternative` |
indicates the alternative hypothesis and must be
one of `"stationary"` (default) or `"explosive"` . You can
specify just the initial letter. |

`type` |
indicates which variant of the test is computed and must
be one of `"Z(alpha)"` (default) or `"Z(t_alpha)"` . |

`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 `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.

### 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. |

`alternative` |
a character string describing the alternative
hypothesis. |

### Author(s)

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.

### See Also

`adf.test`

### Examples

x <- rnorm(1000) # no unit-root
pp.test(x)
y <- cumsum(x) # has unit root
pp.test(y)

[Package

*tseries* version 0.10-0

Index]