white.test

0th

Percentile

White Neural Network Test for Nonlinearity

Generically computes the White 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':
white.test(x, lag = 1, qstar = 2, q = 10, range = 4, type = c("Chisq","F"),
scale = TRUE)
## S3 method for class 'default':
white.test(x, y, qstar = 2, q = 10, range = 4, 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.
q
an integer representing the number of phantom hidden units used to compute the test statistic.
qstar
the test is conducted using qstar principal components of the phantom hidden units. The first principal component is omitted since in most cases it appears to be collinear with the input vector of lagged variables. This strategy p
range
the input to hidden unit weights are initialized uniformly over [-range/2, range/2].
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.
Details

The null is the hypotheses of linearity in mean''. This type of test is consistent against arbitrary nonlinearity in mean. 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. H. Lee, H. White, and C. W. J. Granger (1993): Testing for neglected nonlinearity in time series models. Journal of Econometrics 56, 269-290.

terasvirta.test

Aliases
• white.test
• white.test.ts
• white.test.default
Examples
n <- 1000

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

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

x[1] <- 0.0
for (i in (2:n))  # Generate time series which is nonlinear in mean''
{
x[i] <- 0.4*x[i-1] + tanh(x[i-1]) + rnorm (1, sd=0.5)
}
x <- as.ts(x)
plot (x)
white.test (x)
Documentation reproduced from package tseries, version 0.7-4, License: GPL (see file COPYING), except for ./src/muin2ser.f and ./misc which are free for non-commercial purposes. See file README for details.

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