terasvirta.test

From tseries v0.10-24 by Kurt Hornik

Teraesvirta Neural Network Test for Nonlinearity

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

Keywords: ts

Usage

## S3 method for class 'ts':
terasvirta.test(x, lag = 1, type = c("Chisq","F"),
                scale = TRUE, ...)
## S3 method for class 'default':
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:
statisticthe value of the test statistic.
p.valuethe p-value of the test.
methoda character string indicating what type of test was performed.
parametera list containing the additional parameters used to compute the test statistic.
data.namea character string giving the name of the data.
argumentsadditional arguments used to compute the test statistic.

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)

Documentation reproduced from package tseries, version 0.10-24, License: GPL-2

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