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# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
# Copyrights (C)
# for this R-port:
# 1999 - 2007, Diethelm Wuertz, GPL
# Diethelm Wuertz <wuertz@itp.phys.ethz.ch>
# info@rmetrics.org
# www.rmetrics.org
# for the code accessed (or partly included) from other R-ports:
# see R's copyright and license files
# for the code accessed (or partly included) from contributed R-ports
# and other sources
# see Rmetrics's copyright file
################################################################################
# FUNCTION: DESCRIPION:
# tsTest Time Series Test Suite
# FUNCTION: DEPENDENCY TEST:
# bdsTest Brock-Dechert-Scheinkman test for iid series
# FUNCTION: NONLINEARITY TESTS:
# wnnTest White Neural Network Test for Nonlinearity
# tnnTest Teraesvirta Neural Network Test for Nonlinearity
################################################################################
test.tsSuite =
function()
{
# NA
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.bdsTest =
function()
{
# iid example:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
x = rnorm(100)
plot(x, type = "l", col = "steelblue")
test = bdsTest(x)
print(test)
p.value = as.vector(test@test$p.value)
# Is each of the 8 p.values greater 0.1?
checkEqualsNumeric(sum(p.value > 0.1), 8)
# Not identically distributed:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
x = c(rnorm(50), runif(50))
test = bdsTest(x)
print(test)
p.value = as.vector(test@test$p.value)
# Is each of the 8 p.values smaller 1e-3?
checkEqualsNumeric(sum(p.value < 1e-3), 8)
# Not independent:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
n = 500
x = rep(0, times = n)
for(i in (2:n)) x[i] = 0.4*x[i-1] + tanh(x[i-1]) + rnorm(1, sd = 0.5)
plot(x, type = "l", col = "steelblue")
test = bdsTest(x)
print(test)
p.value = as.vector(test@test$p.value)
# Is each of the 8 p.values smaller 1e-6?
checkEqualsNumeric(sum(p.value < 1e-6), 8)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.wnnTest =
function()
{
# White NN Test:
# See tseries Package:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
x = runif(1000, -1, 1)
plot(x, type = "l", col = "steelblue")
test = wnnTest(x)
print(test)
p.value = as.vector(test@test$p.value)
# Is each of the two p.values greater 0.5?
checkTrue(as.logical(mean(p.value > 0.5)))
## Generate time series which is nonlinear in ``mean''
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
n = 1000
x = rep(0, times = n)
for(i in (2:n)) x[i] <- 0.4*x[i-1] + tanh(x[i-1]) + rnorm(1, sd = 0.5)
plot(x, type = "l", col = "steelblue")
test = wnnTest(x)
print(test)
p.value = as.vector(test@test$p.value)
# Is each of the two p.values smaller than 1e-4?
checkTrue(as.logical(mean(p.value < 1e-4)))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.tnnTest =
function()
{
# Teraesvirta NN Test:
# See example from tseries Package:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
x = runif(1000, -1, 1)
plot(x, type = "l", col = "steelblue")
test = tnnTest(x)
print(test)
p.value = as.vector(test@test$p.value)
# Is each of the two p.values greater 0.5?
checkTrue(as.logical(mean(p.value > 0.5)))
## Generate time series which is nonlinear in ``mean''
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
n = 1000
x = rep(0, times = n)
for(i in (2:n)) x[i] <- 0.4*x[i-1] + tanh(x[i-1]) + rnorm(1, sd = 0.5)
plot(x, type = "l", col = "steelblue")
test = tnnTest(x)
print(test)
p.value = as.vector(test@test$p.value)
# Is each of the two p.values smaller than 1e-4?
checkTrue(as.logical(mean(p.value < 1e-4)))
# Return Value:
return()
}
################################################################################
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