R/DickeyFullerPValues.R
In 42n4/fUnitRoots: Trends and Unit Roots
# 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
# fEcofin-DickeyFullerPValues.R
################################################################################
# FUNCTION: AUGMENTED DICKEY FULLER DATA TABLES:
# adfTable Finite sample p values for the Dickey-Fuller test
# .adfPlot Plots sample p values for the Dickey-Fuller test
# padf Returns probabilities for the ADF Test given quantiles
# qadf Returns quantiles for the ADF Test given probabilities
################################################################################
################################################################################
# FUNCTION: AUGMENTED DICKEY FULLER DATA TABLES:
# adfTable Finite sample p values for the Dickey-Fuller test
# .adfPlot Plots sample p values for the Dickey-Fuller test
# padf Returns probabilities for the ADF Test given quantiles
# qadf Returns quantiles for the ADF Test given probabilities
adfTable =
function(trend = c("nc", "c", "ct"), statistic = c("t", "n"), includeInf = TRUE)
{ # A function implemented by Diethelm Wuertz
# Description:
# Tables critical values for augmented Dickey-Fuller test.
# Note:
# x=-3:0; y=0:3; z=outer(x,y,"*"); rownames(z)=x; colnames(z)=y; z
# Examples:
# adfTable()
# FUNCTION:
# Match Arguments:
type = trend = match.arg(trend)
statistic = match.arg(statistic)
# Tables:
if (statistic == "t") {
# Hamilton Table B.6 - OLS t-Statistic
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))
} else 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))
} else 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))
} else {
stop("Invalid type specified")
}
} else if (statistic == "z" || statistic == "n") {
# Hamilton Table B.5 - Based on OLS Autoregressive Coefficient
if (type == "nc") {
table = cbind(
c(-11.9, -9.3, -7.3, -5.3, +1.01, +1.40, +1.79, +2.28),
c(-12.9, -9.9, -7.7, -5.5, +0.97, +1.35, +1.70, +2.16),
c(-13.3, -10.2, -7.9, -5.6, +0.95, +1.31, +1.65, +2.09),
c(-13.6, -10.3, -8.0, -5.7, +0.93, +1.28, +1.62, +2.04),
c(-13.7, -10.4, -8.0, -5.7, +0.93, +1.28, +1.61, +2.04),
c(-13.8, -10.5, -8.1, -5.7, +0.93, +1.28, +1.60, +2.03))
} else if (type == "c") {
table = cbind(
c(-17.2, -14.6, -12.5, -10.2, -0.76, +0.01, +0.65, +1.40),
c(-18.9, -15.7, -13.3, -10.7, -0.81, -0.07, +0.53, +1.22),
c(-19.8, -16.3, -13.7, -11.0, -0.83, -0.10, +0.47, +1.14),
c(-20.3, -16.6, -14.0, -11.2, -0.84, -0.12, +0.43, +1.09),
c(-20.5, -16.8, -14.0, -11.2, -0.84, -0.13, +0.42, +1.06),
c(-20.7, -16.9, -14.1, -11.3, -0.85, -0.13, +0.41, +1.04))
} else if (type == "ct") {
table = cbind(
c(-22.5, -19.9, -17.9, -15.6, -3.66, -2.51, -1.53, -0.43),
c(-25.7, -22.4, -19.8, -16.8, -3.71, -2.60, -1.66, -0.65),
c(-27.4, -23.6, -20.7, -17.5, -3.74, -2.62, -1.73, -0.75),
c(-28.4, -24.4, -21.3, -18.0, -3.75, -2.64, -1.78, -0.82),
c(-28.9, -24.8, -21.5, -18.1, -3.76, -2.65, -1.78, -0.84),
c(-29.5, -25.1, -21.8, -18.3, -3.77, -2.66, -1.79, -0.87))
} else {
stop("Invalid type specified")
}
} else {
stop("Invalid statistic specified")
}
# Transpose:
Table = t(table)
colnames(Table) = c("0.010", "0.025", "0.050", "0.100", "0.900",
"0.950", "0.975", "0.990")
rownames(Table) = c(" 25", " 50", "100", "250", "500", "Inf")
ans = list(
x = as.numeric(rownames(Table)),
y = as.numeric(colnames(Table)),
z = Table)
class(ans) = "gridData"
# Exclude Inf:
if (!includeInf) {
nX = length(ans$x)
ans$x = ans$x[-nX]
ans$z = ans$z[-nX, ]
}
# Add Control:
attr(ans, "control") <-
c(table = "adf", trend = trend, statistic = statistic)
# Return Value:
ans
}
# ------------------------------------------------------------------------------
.adfPlot =
function(trend = c("nc", "c", "ct"), statistic = c("t", "n"))
{ # A function implemented by Diethelm Wuertz
# Match Arguments:
trend = match.arg(trend)
statistic = match.arg(statistic)
# Load Table:
Y = adfTable(trend, statistic, includeInf = FALSE)
X = cbind(expand.grid(x = Y$x, y = Y$y), z = as.vector(Y$z))
x = X[, 1] # N
y = X[, 3] # q-Stat
z = X[, 2] # p-Value
# Interpolate:
gridPoints = 51
ans = linearInterp(x, y, z,
xo = seq(min(x)-5, max(x)+5, length = gridPoints),
yo = seq(min(y), max(y), length = gridPoints))
# Plot:
persp(ans, theta = 40, xlab = "N", ylab = "q", zlab = "p-Value",
main = paste(trend, "|", statistic, "ADF"))
# Return Value:
invisible(NULL)
}
# ------------------------------------------------------------------------------
padf =
function(q, N = Inf, trend = c("nc", "c", "ct"), statistic = c("t", "n"))
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns probabilities for the ADF Test given quantiles
# Arguments:
# q = quantiles (-2.66 < q < 2.16)
# N = sample sizes
# Example:
# padf(q = -2:2, N = 1000)
# padf(q = -2:2, N = 100)
# padf(q = -2:2, N = 20)
# padf(-3, 100)
# q=c(-2:2); for (N in c(20, 100, 1000, 5000, Inf)) print(padf(q, N))
# FUNCTION:
# Match Arguments:
trend = match.arg(trend)
statistic = match.arg(statistic)
# Check N:
stopifnot(length(N) == 1)
# Grid Points:
X = adfTable(trend = trend, statistic = statistic)
if (N < 500) {
finiteX = cbind(expand.grid(x = X$x, y = X$y), z = as.vector(X$z))
X = finiteX[is.finite(finiteX[, 1]), ]
y = X[, 1] # N
z = X[, 2] # p-Value
x = X[, 3] # q-Stat
# Out Points:
xo = q
yo = rep(N, times = length(q))
# Interpolate:
p = linearInterpp(x, y, z, xo = xo, yo = yo)[, 3]
} else {
x = X$z["Inf", ]
y = X$y
p = approx(x, y, xout = q)$y
}
# Result:
names(p) = as.character(q)
attr(p, "control") <- c(N = N)
# Return Value:
p
}
# ------------------------------------------------------------------------------
qadf =
function(p, N = Inf, trend = c("nc", "c", "ct"), statistic = c("t", "n"))
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns quantiles for the ADF Test given probabilities
# Arguments:
# p = probabilities (0.01 < p < 0.99)
# N = sample sizes
# Example:
# qadf(p = (1:9)/10, N = 1000)
# qadf(p = (1:9)/10, N = 100)
# qadf(p = (1:9)/10, N = 20)
# qadf(0.001, 100)
# qadf(0.001, 1000)
# FUNCTION:
# Match Arguments:
trend = match.arg(trend)
statistic = match.arg(statistic)
# Check N:
stopifnot(length(N) == 1)
# Grid Points:
X = adfTable(trend = trend, statistic = statistic)
if (N < 500) {
finiteX = cbind(expand.grid(x = X$x, y = X$y), z = as.vector(X$z))
X = finiteX[is.finite(finiteX[, 1]), ]
x = X[, 1]
y = X[, 2]
z = X[, 3]
# Out Points:
xo = rep(N, times = length(p))
yo = p
# Interpolate:
q = linearInterpp(x, y, z, xo = xo, yo = yo)[, 3]
} else {
x = X$y
y = X$z["Inf", ]
q = approx(x, y, xout = p)$y
}
# Result:
names(q) = as.character(p)
attr(q, "control") <- c(N = N)
# Return Value:
q
}
################################################################################
42n4/fUnitRoots documentation built on May 20, 2019, 2:21 p.m.
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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
# fEcofin-DickeyFullerPValues.R
################################################################################
# FUNCTION: AUGMENTED DICKEY FULLER DATA TABLES:
# adfTable Finite sample p values for the Dickey-Fuller test
# .adfPlot Plots sample p values for the Dickey-Fuller test
# padf Returns probabilities for the ADF Test given quantiles
# qadf Returns quantiles for the ADF Test given probabilities
################################################################################
################################################################################
# FUNCTION: AUGMENTED DICKEY FULLER DATA TABLES:
# adfTable Finite sample p values for the Dickey-Fuller test
# .adfPlot Plots sample p values for the Dickey-Fuller test
# padf Returns probabilities for the ADF Test given quantiles
# qadf Returns quantiles for the ADF Test given probabilities
adfTable =
function(trend = c("nc", "c", "ct"), statistic = c("t", "n"), includeInf = TRUE)
{ # A function implemented by Diethelm Wuertz
# Description:
# Tables critical values for augmented Dickey-Fuller test.
# Note:
# x=-3:0; y=0:3; z=outer(x,y,"*"); rownames(z)=x; colnames(z)=y; z
# Examples:
# adfTable()
# FUNCTION:
# Match Arguments:
type = trend = match.arg(trend)
statistic = match.arg(statistic)
# Tables:
if (statistic == "t") {
# Hamilton Table B.6 - OLS t-Statistic
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))
} else 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))
} else 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))
} else {
stop("Invalid type specified")
}
} else if (statistic == "z" || statistic == "n") {
# Hamilton Table B.5 - Based on OLS Autoregressive Coefficient
if (type == "nc") {
table = cbind(
c(-11.9, -9.3, -7.3, -5.3, +1.01, +1.40, +1.79, +2.28),
c(-12.9, -9.9, -7.7, -5.5, +0.97, +1.35, +1.70, +2.16),
c(-13.3, -10.2, -7.9, -5.6, +0.95, +1.31, +1.65, +2.09),
c(-13.6, -10.3, -8.0, -5.7, +0.93, +1.28, +1.62, +2.04),
c(-13.7, -10.4, -8.0, -5.7, +0.93, +1.28, +1.61, +2.04),
c(-13.8, -10.5, -8.1, -5.7, +0.93, +1.28, +1.60, +2.03))
} else if (type == "c") {
table = cbind(
c(-17.2, -14.6, -12.5, -10.2, -0.76, +0.01, +0.65, +1.40),
c(-18.9, -15.7, -13.3, -10.7, -0.81, -0.07, +0.53, +1.22),
c(-19.8, -16.3, -13.7, -11.0, -0.83, -0.10, +0.47, +1.14),
c(-20.3, -16.6, -14.0, -11.2, -0.84, -0.12, +0.43, +1.09),
c(-20.5, -16.8, -14.0, -11.2, -0.84, -0.13, +0.42, +1.06),
c(-20.7, -16.9, -14.1, -11.3, -0.85, -0.13, +0.41, +1.04))
} else if (type == "ct") {
table = cbind(
c(-22.5, -19.9, -17.9, -15.6, -3.66, -2.51, -1.53, -0.43),
c(-25.7, -22.4, -19.8, -16.8, -3.71, -2.60, -1.66, -0.65),
c(-27.4, -23.6, -20.7, -17.5, -3.74, -2.62, -1.73, -0.75),
c(-28.4, -24.4, -21.3, -18.0, -3.75, -2.64, -1.78, -0.82),
c(-28.9, -24.8, -21.5, -18.1, -3.76, -2.65, -1.78, -0.84),
c(-29.5, -25.1, -21.8, -18.3, -3.77, -2.66, -1.79, -0.87))
} else {
stop("Invalid type specified")
}
} else {
stop("Invalid statistic specified")
}
# Transpose:
Table = t(table)
colnames(Table) = c("0.010", "0.025", "0.050", "0.100", "0.900",
"0.950", "0.975", "0.990")
rownames(Table) = c(" 25", " 50", "100", "250", "500", "Inf")
ans = list(
x = as.numeric(rownames(Table)),
y = as.numeric(colnames(Table)),
z = Table)
class(ans) = "gridData"
# Exclude Inf:
if (!includeInf) {
nX = length(ans$x)
ans$x = ans$x[-nX]
ans$z = ans$z[-nX, ]
}
# Add Control:
attr(ans, "control") <-
c(table = "adf", trend = trend, statistic = statistic)
# Return Value:
ans
}
# ------------------------------------------------------------------------------
.adfPlot =
function(trend = c("nc", "c", "ct"), statistic = c("t", "n"))
{ # A function implemented by Diethelm Wuertz
# Match Arguments:
trend = match.arg(trend)
statistic = match.arg(statistic)
# Load Table:
Y = adfTable(trend, statistic, includeInf = FALSE)
X = cbind(expand.grid(x = Y$x, y = Y$y), z = as.vector(Y$z))
x = X[, 1] # N
y = X[, 3] # q-Stat
z = X[, 2] # p-Value
# Interpolate:
gridPoints = 51
ans = linearInterp(x, y, z,
xo = seq(min(x)-5, max(x)+5, length = gridPoints),
yo = seq(min(y), max(y), length = gridPoints))
# Plot:
persp(ans, theta = 40, xlab = "N", ylab = "q", zlab = "p-Value",
main = paste(trend, "|", statistic, "ADF"))
# Return Value:
invisible(NULL)
}
# ------------------------------------------------------------------------------
padf =
function(q, N = Inf, trend = c("nc", "c", "ct"), statistic = c("t", "n"))
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns probabilities for the ADF Test given quantiles
# Arguments:
# q = quantiles (-2.66 < q < 2.16)
# N = sample sizes
# Example:
# padf(q = -2:2, N = 1000)
# padf(q = -2:2, N = 100)
# padf(q = -2:2, N = 20)
# padf(-3, 100)
# q=c(-2:2); for (N in c(20, 100, 1000, 5000, Inf)) print(padf(q, N))
# FUNCTION:
# Match Arguments:
trend = match.arg(trend)
statistic = match.arg(statistic)
# Check N:
stopifnot(length(N) == 1)
# Grid Points:
X = adfTable(trend = trend, statistic = statistic)
if (N < 500) {
finiteX = cbind(expand.grid(x = X$x, y = X$y), z = as.vector(X$z))
X = finiteX[is.finite(finiteX[, 1]), ]
y = X[, 1] # N
z = X[, 2] # p-Value
x = X[, 3] # q-Stat
# Out Points:
xo = q
yo = rep(N, times = length(q))
# Interpolate:
p = linearInterpp(x, y, z, xo = xo, yo = yo)[, 3]
} else {
x = X$z["Inf", ]
y = X$y
p = approx(x, y, xout = q)$y
}
# Result:
names(p) = as.character(q)
attr(p, "control") <- c(N = N)
# Return Value:
p
}
# ------------------------------------------------------------------------------
qadf =
function(p, N = Inf, trend = c("nc", "c", "ct"), statistic = c("t", "n"))
{ # A function implemented by Diethelm Wuertz
# Description:
# Returns quantiles for the ADF Test given probabilities
# Arguments:
# p = probabilities (0.01 < p < 0.99)
# N = sample sizes
# Example:
# qadf(p = (1:9)/10, N = 1000)
# qadf(p = (1:9)/10, N = 100)
# qadf(p = (1:9)/10, N = 20)
# qadf(0.001, 100)
# qadf(0.001, 1000)
# FUNCTION:
# Match Arguments:
trend = match.arg(trend)
statistic = match.arg(statistic)
# Check N:
stopifnot(length(N) == 1)
# Grid Points:
X = adfTable(trend = trend, statistic = statistic)
if (N < 500) {
finiteX = cbind(expand.grid(x = X$x, y = X$y), z = as.vector(X$z))
X = finiteX[is.finite(finiteX[, 1]), ]
x = X[, 1]
y = X[, 2]
z = X[, 3]
# Out Points:
xo = rep(N, times = length(p))
yo = p
# Interpolate:
q = linearInterpp(x, y, z, xo = xo, yo = yo)[, 3]
} else {
x = X$y
y = X$z["Inf", ]
q = approx(x, y, xout = p)$y
}
# Result:
names(q) = as.character(p)
attr(q, "control") <- c(N = N)
# Return Value:
q
}
################################################################################
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