tseries: terasvirta.test – R documentation

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terasvirta.test

Teraesvirta Neural Network Test for Nonlinearity

Description

Generically computes Teraesvirta's neural network test for neglected nonlinearity either for the time series x or the regression y~x.

Usage

## S3 method for class 'ts'
terasvirta.test(x, lag = 1, type = c("Chisq","F"),
                scale = TRUE, ...)
## Default S3 method:
terasvirta.test(x, y, type = c("Chisq","F"),
                scale = TRUE, ...)

Arguments

`x`	a numeric vector, matrix, or time series.
`y`	a numeric vector.
`lag`	an integer which specifies the model order in terms of lags.
`type`	a string indicating whether the Chi-Squared test or the F-test is computed. Valid types are `"Chisq"` and `"F"`.
`scale`	a logical indicating whether the data should be scaled before computing the test statistic. The default arguments to `scale` are used.
`...`	further arguments to be passed from or to methods.

Details

The null is the hypotheses of linearity in “mean”. This test uses a Taylor series expansion of the activation function to arrive at a suitable test statistic. If type equals "F", then the F-statistic instead of the Chi-Squared statistic is used in analogy to the classical linear regression.

Missing values are not allowed.

Value

A list with class "htest" containing the following components:

`statistic`	the value of the test statistic.
`p.value`	the p-value of the test.
`method`	a character string indicating what type of test was performed.
`parameter`	a list containing the additional parameters used to compute the test statistic.
`data.name`	a character string giving the name of the data.
`arguments`	additional arguments used to compute the test statistic.

Author(s)

A. Trapletti

References

T. Teraesvirta, C. F. Lin, and C. W. J. Granger (1993): Power of the Neural Network Linearity Test. Journal of Time Series Analysis 14, 209-220.

Examples

n <- 1000

x <- runif(1000, -1, 1)  # Non-linear in ``mean'' regression 
y <- x^2 - x^3 + 0.1*rnorm(x)
terasvirta.test(x, y)

## Is the polynomial of order 2 misspecified?
terasvirta.test(cbind(x,x^2,x^3), y)

## Generate time series which is nonlinear in ``mean''
x[1] <- 0.0
for(i in (2:n)) {
  x[i] <- 0.4*x[i-1] + tanh(x[i-1]) + rnorm(1, sd=0.5)
}
x <- as.ts(x)
plot(x)
terasvirta.test(x)

tseries

Time Series Analysis and Computational Finance

v0.10-48

GPL-2

Authors

Adrian Trapletti [aut], Kurt Hornik [aut, cre], Blake LeBaron [ctb] (BDS test code)

Initial release