PP.test {ts} | 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. |

### 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.

### Examples

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