Weighted.LM.test {WeightedPortTest} | R Documentation |
Weighted Portmanteau Test for Fitted ARCH process
Description
A weighted portmanteau test for testing the null hypothesis of adequately fitted ARCH process. This is essentially a weighted version of the statistic proposed by Li and Mak (1994)
Usage
Weighted.LM.test(x, h.t, lag = 1,
type = c("correlation", "partial"),
fitdf = 1, weighted = TRUE)
Arguments
x |
a numeric vector or univariate time series, or residuals of a fitted time series |
h.t |
a numeric vector of the conditional variances |
lag |
the statistic will be based on |
type |
type of test to be performed, either based on the autocorrelations or partial-autocorrelations. |
fitdf |
the number of ARCH parameters fit to the model, default=1 since at least some ARCH model must be fit to find h.t |
weighted |
A flag determining if the weighting scheme should be utilized. TRUE by default, if FALSE, it performs the test from Li and Mak (1994) |
Details
These test can be performed after fitting an ARCH process to a time series. The theoretical work was originally developed in Li and Mak (1994) and has recently been extended in Fisher and Gallagher (2012).
Value
A list with class "htest
" containing the following components:
statistic |
the value of the test statistic |
parameter |
The approximate shape and scale parameters for the weighted statistic or degrees of freedom of the chi-squared distribution if the weighted flag is set to FALSE. |
p.value |
The p-value of the test |
method |
a character string indicating which type of test was performed. |
data.name |
a character string giving the name of the data |
Note
Similiar to the Box.test()
and Weighted.Box.test()
functions
Author(s)
Thomas J. Fisher
References
Fisher, T. J. and Gallagher, C. M. (2012), New Weighted Portmanteau Statistics for Time Series Goodness-of-Fit Testing. Journal of the American Statistical Association, 107(498), 777-787.
Li, W. K. and Mak, T. K. (1994), On the squared residual autocorrelations in non-linear time series with conditional heteroskedasticity. Journal of Time Series Analysis 15(6), 627-636.