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149 2011 1 0.5 1 0.67 0.67 0 0
139 2011 1 0.5 0.89 0.83 0.33 0.5 1
148 2011 1 0.4 0.89 1 0.67 0 0
158 2011 1 0.5 0.89 0.83 0 0 1
128 2011 1 0.7 0.89 0.67 0 1 1
224 2011 1 0.3 0.78 0 0 0.5 1
159 2011 1 0.4 0.89 0.83 0.67 0.5 0
105 2011 1 0.4 1 0.5 0.67 1 1
159 2011 1 0.7 0.89 0.83 0 0.5 1
167 2011 1 0.6 0.78 0.33 0.67 0.5 1
165 2011 1 0.6 1 0.5 1 0 1
159 2011 1 0.2 0.78 0.67 0 0.5 1
119 2011 1 0.4 0.89 1 0 0.5 1
176 2011 1 0.4 0.89 0.5 0.67 0 0
54 2011 1 0.5 0.89 0.67 0.33 0 0
91 2011 0 0.3 0.89 0.17 0.67 0 0
163 2011 1 0.4 0.89 0.83 0.33 0.5 1
124 2011 1 0.7 0.67 0.67 0.33 0.5 0
137 2011 0 0.5 1 0.67 0.33 0 1
121 2011 1 0.2 0.78 0.67 0 0 0
153 2011 1 0.3 0.78 0.5 0.67 0 1
148 2011 1 0.6 0.89 1 0.33 0 1
221 2011 1 0.6 0.78 0.83 0.33 0 0
188 2011 1 0.2 0.89 0.83 0.33 0 1
149 2011 1 0.7 0.89 1 0.67 1 1
244 2011 1 0.2 0.33 0.67 0 0 1
148 2011 0 1 1 1 0.33 1 1
92 2011 0 0.4 0.89 0.83 0.67 0 0
150 2011 1 0.4 0.89 1 1 0 1
153 2011 1 0.2 0.67 0.83 0.67 0 0
94 2011 1 0.4 0.56 0.67 0.33 0 0
156 2011 1 0.4 0.89 0.67 0 0.5 0
132 2011 1 0.7 0.89 1 0.67 0.5 1
161 2011 1 0.2 1 0.67 0.67 0 1
105 2011 1 0.6 0.78 1 1 0 1
97 2011 1 0.3 0.78 1 1 0.5 1
151 2011 1 0.3 0.33 0.5 0.33 0 0
131 2011 0 0.2 0.78 0.67 0 0.5 1
166 2011 1 0.5 0.89 0.83 0.67 0.5 1
157 2011 1 0.7 0.89 1 0.67 0.5 0
111 2011 1 0.6 0.78 1 0.67 0.5 1
145 2011 1 0.4 0.89 1 0.67 0.5 1
162 2011 1 0.6 0.89 1 0.33 0.5 1
163 2011 1 0.4 1 1 1 0 1
59 2011 0 0.3 0.67 0.83 0.67 0 1
187 2011 1 0.5 1 0.83 0.67 0.5 0
109 2011 1 0.2 0.89 0.5 0 0 1
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105 2011 1 0.5 0.89 0.17 0 0 0
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116 2011 0 0.4 0.89 1 0.67 1 1
42 2011 0 0.3 0.78 1 0 0 1
148 2011 1 0.2 0.78 0.67 0.67 1 1
155 2011 0 0.5 1 1 0 0 1
125 2011 1 0.4 0.78 1 0 0.5 1
116 2011 1 0.6 1 1 0.67 1 1
128 2011 0 0.4 0.78 0.83 1 0 0
138 2011 1 0.4 0.67 0.33 0 0 1
49 2011 0 0.2 0.33 0.33 0.33 0 0
96 2011 0 0.9 1 1 0.67 0.5 1
164 2011 1 0.8 1 1 0.67 1 1
162 2011 1 0.8 0.78 0.83 0 0.5 0
99 2011 1 0.3 0.67 1 1 0.5 0
202 2011 1 0.2 1 0.83 0.67 0 1
186 2011 1 0.4 0.89 0.67 0 0.5 0
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214 2011 1 0.1 1 0.83 0.67 0 1
188 2011 1 0.4 0.56 0.67 1 0.5 1
104 2011 0 0.5 0.67 1 0 0.5 0
177 2011 1 0.8 0.89 0.83 0.33 0.5 0
126 2011 1 0.4 0.89 0.67 0.67 0 0
76 2011 0 0.6 0.89 0.83 0.33 0.5 0
99 2011 0 0.5 0.89 0.83 0.67 0.5 1
139 2011 1 0.3 0.78 0.67 0 0 0
162 2011 1 0.4 1 0.33 0 0.5 0
108 2011 0 0.6 1 0.83 0.67 0.5 1
159 2011 1 0.4 0.89 1 0.33 0 0
74 2011 0 0.3 0.44 0.83 0 0 0
110 2011 1 0.8 0.78 0.83 0 1 1
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116 2011 0 0.3 0.67 0.5 0 0 0
87 2011 0 0.5 0.78 0.83 0.67 0.5 0
97 2011 0 0.4 0.78 1 0.33 0 1
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106 2011 0 0.7 0.89 1 0.33 0 1
80 2011 0 0.2 0.89 0.67 0.33 0.5 1
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133 2011 0 0.6 0.56 0.83 0 0 0
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126 2011 0 0.8 1 0.83 0.33 0.5 1
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52 2011 0 0.3 0.89 0.5 0.67 0 0
98 2011 0 0.5 0.67 1 0 0 0
118 2011 0 0.4 0.89 0.67 0.67 0 0
99 2011 0 0.4 0.67 0.83 0.67 0 1
48 2012 1 0.6 0.78 1 0 1 1
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150 2012 1 0.4 0.78 1 0.33 1 1
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109 2012 0 1 0.78 1 1 1 0
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158 2012 1 0.3 0.89 1 0.67 1 0
159 2012 1 0.5 1 0.83 0.67 0 1
67 2012 1 0.4 0.78 1 0 0 0
147 2012 1 0.3 0.67 0.83 0.67 0 0
39 2012 1 0.5 0.89 0.83 1 0 1
100 2012 1 0.3 0.67 1 0.67 0 1
111 2012 1 0.3 0.67 0.67 0 0 1
138 2012 1 0.4 1 0.83 0 0 1
101 2012 1 0.3 0.67 1 0 0 1
131 2012 0 0.6 1 1 0.33 0.5 1
101 2012 1 0.6 0.89 0.83 0.67 1 1
114 2012 1 0.4 0.89 1 1 1 1
165 2012 1 0.4 1 1 0 0 0
114 2012 1 0.4 0.67 1 0.67 0 1
111 2012 1 0.3 0.44 0.67 0.67 0.5 1
75 2012 1 0.2 0.89 1 0.33 1 1
82 2012 1 0.5 0.56 0.83 0.67 0 1
121 2012 1 0.4 0.78 1 0.67 1 1
32 2012 1 0.4 1 1 0.67 0 1
150 2012 1 0.4 1 0.83 0.67 0 0
117 2012 1 0.3 0.89 0.67 0.67 0.5 1
71 2012 0 0.4 0.67 0.83 0.67 1 1
165 2012 1 0.2 0.89 1 0.33 0.5 1
154 2012 1 0 0.33 0 0 0 1
126 2012 1 0.4 0.89 1 0.67 0.5 1
149 2012 1 0.6 0.78 1 0 1 0
145 2012 1 0.4 1 0.67 0.67 0 0
120 2012 1 0.4 0.44 1 0 0 1
109 2012 1 0.4 0.67 0.83 0 0.5 0
132 2012 1 0.2 0.33 0.17 0 0.5 0
172 2012 1 0.4 0.89 0.83 1 1 1
169 2012 1 0.3 0.89 0.83 0 0 0
114 2012 1 0.6 1 0.83 0.67 1 1
156 2012 1 0.6 0.89 0.83 1 0 1
172 2012 1 0.4 0.89 0.83 0 0 0
68 2012 0 0.5 1 1 0.67 1 1
89 2012 0 0.4 0.89 0.83 0 0.5 1
167 2012 1 0.6 1 1 1 1 1
113 2012 1 0.6 0.78 0.83 0.67 0.5 0
115 2012 0 0.9 0.78 1 0.67 0.5 0
78 2012 0 0.4 0.67 0.83 0.67 0.5 0
118 2012 0 0.8 0.89 1 1 0.5 0
87 2012 0 0.5 0.67 0.83 1 0 1
173 2012 1 0.4 0.78 0.83 1 0 0
2 2012 1 0.4 0.89 1 0.67 1 1
162 2012 0 0.7 0.89 1 1 1 0
49 2012 0 0.4 0.78 1 0.33 1 1
122 2012 0 0.8 1 1 0.67 0.5 0
96 2012 0 0.4 1 1 1 1 1
100 2012 0 0.3 1 1 0.67 0 0
82 2012 0 0.5 0.67 1 0.67 0.5 0
100 2012 0 0.8 0.89 1 0.67 1 1
115 2012 0 0.4 1 0.83 0.33 0 0
141 2012 0 1 1 1 1 0.5 1
165 2012 1 0.5 0.89 1 0.67 1 1
165 2012 1 0.5 0.89 1 0.67 1 1
110 2012 0 0.3 0.89 1 0.33 0 1
118 2012 1 0.3 0.89 0.83 0.33 0.5 1
158 2012 1 0.3 0.89 0.5 0 0 0
146 2012 0 0.4 1 0.67 0.33 0.5 1
49 2012 1 0.5 0.67 1 0.33 0 0
90 2012 0 0.5 1 0.67 0.67 0.5 0
121 2012 0 0.4 0.89 1 0 0 0
155 2012 1 0.7 0.89 1 1 0.5 1
104 2012 0 0.5 0.89 0.5 0.33 0 0
147 2012 0 0.4 0.89 0.67 0.33 1 1
110 2012 0 0.7 1 0.67 1 0 0
108 2012 0 0.7 1 0.67 1 0 0
113 2012 0 0.7 1 0.67 1 0 0
115 2012 0 0.7 0.89 0.67 1 0 0
61 2012 0 0.7 0.89 0.67 0 0 1
60 2012 0 0.7 0.89 1 0.67 0.5 1
109 2012 0 0.1 0.33 0.67 0.33 0.5 1
68 2012 0 0.2 0.67 0.67 0.67 0.5 1
111 2012 0 0.3 0.56 0.33 0.33 0 0
77 2012 0 0.6 0.44 0.83 0.33 0 0
73 2012 0 0.8 1 1 1 1 1
151 2012 1 0.8 0.89 1 0.33 0.5 0
89 2012 0 0 0.33 0.17 0 0 0
78 2012 0 0.3 0.67 0.67 0.33 0 0
110 2012 0 0.6 0.67 0.83 0.33 0.5 0
220 2012 1 0.5 1 0.83 0.67 0 1
65 2012 0 0.7 0.78 1 0.33 0 1
141 2012 1 0.3 0.67 0.83 0 0.5 0
117 2012 0 0.3 1 1 0.67 0 0
122 2012 1 0.4 0.78 1 0.67 0 1
63 2012 0 0.4 0.89 0.83 1 0 0
44 2012 1 0.1 0.89 0.83 0 0 1
52 2012 0 0.5 0.89 1 0.67 0 1
131 2012 0 0 0 0 0 0 0
101 2012 0 0.4 0.67 1 0.33 0.5 1
42 2012 0 0.6 1 0.83 0.67 1 1
152 2012 1 0.4 1 1 0.33 0.5 1
107 2012 1 0.1 0.67 0.33 0 0.5 0
77 2012 0 0.3 0.89 0.83 0 0 0
154 2012 1 0.7 0.89 0.83 0.67 0 0
103 2012 1 0.3 0.56 0.17 0 0 1
96 2012 0 0.5 0.67 0.83 0.33 0.5 1
175 2012 1 0.3 1 0.83 0.67 1 1
57 2012 0 0.6 1 0.67 0.67 0.5 1
112 2012 0 0.9 1 1 1 0 0
143 2012 1 0.4 0.67 0.83 0 0.5 0
49 2012 0 0.3 0.44 1 0 0.5 0
110 2012 1 0.9 0.89 1 0.67 1 1
131 2012 1 0.5 0.44 1 0 0.5 1
167 2012 1 0.3 0.56 1 1 0.5 0
56 2012 0 0.6 0.89 0.83 0.67 0 0
137 2012 1 0.2 0.67 1 0.33 0 0
86 2012 0 0.4 0.89 0.83 1 0.5 1
121 2012 1 0.5 1 0.83 0.67 0.5 1
149 2012 1 0.4 0.78 0.83 0.67 0 0
168 2012 1 0 0.44 0 0 0 0
140 2012 1 0.2 0.89 1 0.33 0.5 0
88 2012 0 0.5 0.89 1 0.67 0.5 1
168 2012 1 0.3 0.89 1 0.67 0 1
94 2012 1 0 0.44 0 0 0 1
51 2012 1 0.5 1 0.83 1 0 1
48 2012 0 0.6 0.89 0.83 0.33 0 0
145 2012 1 0.3 0.67 0.83 0 0.5 1
66 2012 1 0 0.33 0 0 0 1
85 2012 0 0.3 0.78 0.67 0 0.5 1
109 2012 1 0.5 0.89 1 0.67 0.5 0
63 2012 0 0.4 0.78 0.67 0 0 0
102 2012 0 0.5 0.78 0.83 0.67 0 1
162 2012 0 0.7 0.89 1 1 1 0
86 2012 0 0.8 0.78 1 0.67 0.5 1
114 2012 0 0.6 0.78 1 0.33 0.5 1
164 2012 1 0.4 0.67 0.83 0.33 0 0
119 2012 1 0.5 0.89 0.83 0.33 0.5 1
126 2012 1 0.5 0.89 1 0 0.5 0
132 2012 1 0.3 0.78 1 0.33 0 1
142 2012 1 0.6 1 1 0 0.5 1
83 2012 1 0.3 1 0.67 0.67 0 0
94 2012 0 0.6 0.78 0.83 1 0.5 1
81 2012 0 0.3 0.78 0.33 0.33 0 0
166 2012 1 0.7 0.89 1 0.67 1 1
110 2012 0 0.7 0.89 1 1 0 0
64 2012 0 0.6 0.67 0.67 1 0.5 1
93 2012 1 0.5 1 1 0.33 0.5 0
104 2012 0 0.5 0.67 0.83 0.33 0 0
105 2012 0 0.4 0.56 0.67 0 0 1
49 2012 0 0.4 0.78 1 0.33 1 1
88 2012 0 0.7 1 1 1 0 0
95 2012 0 0.2 0.67 0.17 0 0.5 1
102 2012 0 0.5 0.78 0.83 0.67 0 1
99 2012 0 0.4 0.56 0.83 0.67 0.5 0
63 2012 0 0.2 1 1 0.67 1 1
76 2012 0 0.5 0.89 0.67 0.67 0 0
109 2012 0 0.4 0.44 0.5 0 0 0
117 2012 0 0.7 1 0.67 1 1 1
57 2012 0 0.6 0.89 0.83 0.67 1 1
120 2012 0 0.4 0.78 0.83 0 0 0
73 2012 0 0.5 0.89 1 0.67 1 1
91 2012 0 0 0.11 0.17 0 0 0
108 2012 0 0.7 0.89 1 0.67 0.5 0
105 2012 0 0.4 0.89 0.67 0.67 0 1
117 2012 1 0.5 1 0.67 1 0 0
119 2012 0 0.6 0.89 0.83 0.67 0 0
31 2012 0 0.8 1 0.5 0.67 0.5 1

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')