white.test | R Documentation |
Generically computes the White neural network test for neglected
nonlinearity either for the time series x
or the regression
y~x
.
## S3 method for class 'ts'
white.test(x, lag = 1, qstar = 2, q = 10, range = 4,
type = c("Chisq","F"), scale = TRUE, ...)
## Default S3 method:
white.test(x, y, qstar = 2, q = 10, range = 4,
type = c("Chisq","F"), scale = TRUE, ...)
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 |
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 |
scale |
a logical indicating whether the data should be scaled
before computing the test statistic. The default arguments to
|
... |
further arguments to be passed from or to methods. |
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.
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. |
A. Trapletti
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
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)
## Is the polynomial of order 2 misspecified?
white.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)
white.test(x)
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