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 ChiSquared test or the
Ftest 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 Fstatistic
instead of the ChiSquared 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 pvalue 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, 269290.
terasvirta.test
n < 1000
x < runif(1000, 1, 1) # Nonlinear 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[i1] + tanh(x[i1]) + rnorm(1, sd=0.5)
}
x < as.ts(x)
plot(x)
white.test(x)
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