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# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2, or (at your option)
# any later version.
#
# This program 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
# General Public License for more details.
#
# A copy of the GNU General Public License is available via WWW at
# http://www.gnu.org/copyleft/gpl.html. You can also obtain it by
# writing to the Free Software 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: ADF TESTS:
# adfTest ADF unit root test using Banarjee's test statistics
# unitrootTest ADF unit root test using McKinnon's test statistics
# FUNCTION: UNITROOT TEST SUITE:
# .urTest Unit Root Test Suite
################################################################################
adfTest =
function(x, lags = 1, type = c("nc", "c", "ct"), title = NULL,
description = NULL)
{ # A function implemented by Diethelm Wuertz
# Description:
# Tests the null hypothesis of a unit root in y.
# Arguments:
# x - numeric vector
# type - specifies the regression model to be estimatied and the
# null hypothesis, "nc" no constant and no trend, "c" add
# constant, "ct" add constant and trend.
# lags - specifies the number of lagged differences of x to be
# included in the regression model. If 'lags' = h, a term
# sum_{i=1}^{h-1} beta_i * diff(x)_(t-h) is added to the
# regression equation.
# Value:
# A list of class "htest" containing the following components:
# statistic - the value of the test statistic (t-statistic)
# parameter - the number of lags.
# p.value - the p-value of the test
# method - a character string indicating what type of test was performed
# data.name - a character string giving the name of the data y
# Reference:
# S. E. SAID and D. A. DICKEY (1984): Testing for Unit Roots in
# Autoregressive-Moving Average Models of Unlag.diffnown Order.
# Biometrika 71, 599607.
# Source:
# This function is an augmented version of Adrian Trapletti's
# function adf.test() which considers type "ct" only. We have added
# the types "c" and "nc" together with the appropriate statistics.
# Call:
CALL = match.call()
# Test:
test = list()
# Data Set Name:
DNAME = deparse(substitute(x))
test$data.name = DNAME
# Transform:
if (class(x) == "timeSeries") x = series(x)
x = as.vector(x)
# Check Arguments:
if (lags < 0) stop("Lags are negative")
# Settings:
doprint = FALSE
type = type[1]
lags = lags + 1
y = diff(x)
n = length(y)
z = embed(y, lags)
y.diff = z[, 1]
y.lag.1 = x[lags:n]
tt = lags:n
# Regression:
if (lags > 1) {
y.diff.lag = z[,2:lags]
if (type == "nc"){
res = lm(y.diff ~ y.lag.1 - 1 + y.diff.lag) }
if (type == "c"){
res = lm(y.diff ~ y.lag.1 + 1 + y.diff.lag) }
if (type == "ct") {
res = lm(y.diff ~ y.lag.1 + 1 + tt + y.diff.lag) }
} else {
if (type == "nc") {
res = lm(y.diff ~ y.lag.1 - 1) }
if (type == "c"){
res = lm(y.diff ~ y.lag.1 + 1) }
if (type == "ct") {
res = lm(y.diff ~ y.lag.1 + 1 + tt) }
}
# Regression Summary:
res.sum = summary(res)
if (doprint) print(res.sum)
# Statistic:
if (type == "nc") coefNum = 1 else coefNum = 2
STAT = res.sum$coefficients[coefNum, 1] / res.sum$coefficients[coefNum, 2]
names(STAT) = "Dickey-Fuller"
test$statistic = STAT
# P Value:
if (type == "nc")
table = cbind(
c(-2.66, -2.26, -1.95, -1.60, +0.92, +1.33, +1.70, +2.16),
c(-2.62, -2.25, -1.95, -1.61, +0.91, +1.31, +1.66, +2.08),
c(-2.60, -2.24, -1.95, -1.61, +0.90, +1.29, +1.64, +2.03),
c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.29, +1.63, +2.01),
c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.28, +1.62, +2.00),
c(-2.58, -2.23, -1.95, -1.62, +0.89, +1.28, +1.62, +2.00))
if (type == "c")
table = cbind(
c(-3.75, -3.33, -3.00, -2.63, -0.37, +0.00, +0.34, +0.72),
c(-3.58, -3.22, -2.93, -2.60, -0.40, -0.03, +0.29, +0.66),
c(-3.51, -3.17, -2.89, -2.58, -0.42, -0.05, +0.26, +0.63),
c(-3.46, -3.14, -2.88, -2.57, -0.42, -0.06, +0.24, +0.62),
c(-3.44, -3.13, -2.87, -2.57, -0.43, -0.07, +0.24, +0.61),
c(-3.43, -3.12, -2.86, -2.57, -0.44, -0.07, +0.23, +0.60))
if (type == "ct")
table = cbind(
c(-4.38, -3.95, -3.60, -3.24, -1.14, -0.80, -0.50, -0.15),
c(-4.15, -3.80, -3.50, -3.18, -1.19, -0.87, -0.58, -0.24),
c(-4.04, -3.73, -3.45, -3.15, -1.22, -0.90, -0.62, -0.28),
c(-3.99, -3.69, -3.43, -3.13, -1.23, -0.92, -0.64, -0.31),
c(-3.98, -3.68, -3.42, -3.13, -1.24, -0.93, -0.65, -0.32),
c(-3.96, -3.66, -3.41, -3.12, -1.25, -0.94, -0.66, -0.33))
table = t(table)
tablen = dim(table)[2]
tableT = c(25, 50, 100, 250, 500, 1e+05)
tablep = c(0.01, 0.025, 0.05, 0.1, 0.9, 0.95, 0.975, 0.99)
tableipl = numeric(tablen)
for (i in (1:tablen))
tableipl[i] = approx(tableT, table[, i], n, rule = 2)$y
PVAL = approx(tableipl, tablep, STAT, rule = 2)$y
if (is.na(approx(tableipl, tablep, STAT, rule = 1)$y)) {
if (PVAL == min(tablep)) {
warning("p-value smaller than printed p-value")
} else {
warning("p-value greater than printed p-value")
}
}
names(PVAL) = ""
test$p.value = PVAL
# Parameter:
PARAMETER = lags - 1
names(PARAMETER) = "Lag Order"
test$parameter = PARAMETER
# Add:
if (is.null(title)) title = "Augmented Dickey-Fuller Test"
if (is.null(description)) description = date()
# Add Regression:
test$lm = res
# Return Value:
new("fHTEST",
call = CALL,
data = list(x = x),
test = test,
title = as.character(title),
description = description()
)
}
# ------------------------------------------------------------------------------
unitrootTest =
function(x, lags = 1, type = c("nc", "c", "ct"), title = NULL,
description = NULL)
{ # A function implemented by Diethelm Wuertz
# Description:
# Tests the null hypothesis of a unit root in x.
# Arguments:
# x - numeric vector
# type - specifies the regression model to be estimatied and the
# null hypothesis, "nc" no constant and no trend, "c" add
# constant, "ct" add constant and trend.
# lags - specifies the number of lagged differences of x to be
# included in the regression model. If 'lags' = h, a term
# sum_{i=1}^{h-1} beta_i * diff(x)_(t-h) is added to the
# regression equation.
# Value:
# A list with class "htest" containing the following components:
# statistic - the value of the test statistic (t-statistic)
# parameter - the number of lags.
# p.value - the p-value of the test
# method - a character string indicating what "trend" type of
# the test was performed
# data.name - a character string giving the name of the data y
# Reference:
# Said S.E., Dickey D.A. (1984): Testing for Unit Roots in
# Autoregressive-Moving Average Models of Unlag.diffnown Order.
# Biometrika 71, 599-607.
# Source:
# This function is an augmented version of Adrian Trapletti's
# function adf.test() which considers trend "ct" only. We have added
# the trend types "c" and "nc" together with the appropriate statistics.
# FUNCTION:
# Call:
CALL = match.call()
# Test:
test = list()
# Data Set Name:
DNAME = deparse(substitute(x))
test$data.name = DNAME
# Transform:
if (class(x) == "timeSeries") x = series(x)
x = as.vector(x)
# Check Arguments:
if (lags < 0) stop("Lags are negative")
# Settings:
type = type[1]
lags = lags + 1
y = diff(x)
n = length(y)
z = embed(y, lags)
y.diff = z[, 1]
y.lag.1 = x[lags:n]
tt = lags:n
# Regression:
if (lags > 1) {
y.diff.lag = z[,2:lags]
if (type == "nc"){
res = lm(y.diff ~ y.lag.1 - 1 + y.diff.lag) }
if (type == "c"){
res = lm(y.diff ~ y.lag.1 + 1 + y.diff.lag) }
if (type == "ct") {
res = lm(y.diff ~ y.lag.1 + 1 + tt + y.diff.lag) }
if (type == "ctt") {
res = lm(y.diff ~ y.lag.1 + 1 + tt + tt^2 + y.diff.lag) }
} else {
if (type == "nc") {
res = lm(y.diff ~ y.lag.1 - 1) }
if (type == "c"){
res = lm(y.diff ~ y.lag.1 + 1) }
if (type == "ct") {
res = lm(y.diff ~ y.lag.1 + 1 + tt) }
if (type == "ctt") {
res = lm(y.diff ~ y.lag.1 + 1 + tt + tt^2) }
}
res.sum = summary(res)
test$regression = res.sum
# Statistic:
if (type == "nc") coefNum = 1 else coefNum = 2
STATISTIC =
res.sum$coefficients[coefNum, 1] / res.sum$coefficients[coefNum, 2]
names(STATISTIC) = "DF"
test$statistic = STATISTIC
# P Value:
if (type == "nc") { itv = 1 }
if (type == "c") { itv = 2 }
if (type == "ct") { itv = 3 }
if (type == "ctt"){ itv = 4 }
# Statistic == "t" : itt = 1
PVAL1 =
.urcval(arg = STATISTIC, nobs = n, niv = 1, itt = 1, itv = itv, nc = 2)
# Statistic == "n" : itt = 2
PVAL2 =
.urcval(arg = STATISTIC, nobs = n, niv = 1, itt = 2, itv = itv, nc = 2)
PVAL = c(PVAL1, PVAL2)
names(PVAL) = c("t", "n")
test$p.value = PVAL
# Parameter:
PARAMETER = lags - 1
names(PARAMETER) = "Lag Order"
test$parameter = PARAMETER
# Add:
if (is.null(title)) title = "Augmented Dickey-Fuller Test"
if (is.null(description)) description = date()
# Return Value:
new("fHTEST",
call = CALL,
data = list(x = x),
test = test,
title = as.character(title),
description = description()
)
}
################################################################################
.urTest =
function(x, method = c("unitroot", "adf", "urers", "urkpss", "urpp",
"ursp", "urza"), title = NULL, description = NULL, ...)
{ # A function implemented by Diethelm Wuertz
# Description:
# Unit Root Test Suite
# FUNCTION:
# Match Function:
funTest = match.fun(paste(method[1], "Test", sep = ""))
# Test:
ans = funTest(x = x, ...)
# Add:
if (!is.null(title)) ans@title = as.character(title)
if (!is.null(description)) ans@description = description()
# Return Value:
ans
}
################################################################################
|
