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Data X:
10 43 44 124 15 26 - 262 12 50 54 97 22 29 3 267 10 88 71 165 39 39 2 414 7 58 61 139 28 20 - 313 9 59 44 118 24 30 1 285 11 70 59 155 25 35 - 355 13 75 54 137 41 33 - 353 12 51 46 143 31 25 1 309 8 65 57 159 25 38 1 353 11 49 47 130 29 15 3 284 18 62 56 147 38 30 - 351 15 74 68 134 18 28 - 337 14 46 72 132 25 24 1 314 10 52 60 138 35 21 1 317 9 71 77 169 36 26 1 389 2 61 45 116 33 19 1 277 12 56 58 107 27 22 - 282 9 91 89 124 26 24 2 365 11 50 37 124 22 21 1 266 14 58 56 153 26 39 1 347 8 50 66 136 29 25 - 314 4 49 56 117 25 19 - 270 14 56 54 170 24 34 1 353 9 39 38 127 32 20 - 265 10 45 53 127 29 29 - 293 9 26 64 130 34 14 1 278 10 51 68 125 45 19 1 319 9 50 58 124 20 11 - 272 3 43 46 121 41 15 2 271 3 47 41 118 36 25 2 272 4 36 43 128 28 22 - 261 6 46 45 125 35 17 1 275 11 43 43 122 31 15 4 269 19 41 57 135 37 18 1 308 11 38 48 145 27 21 - 290 9 26 34 111 20 21 2 223 7 49 56 157 34 10 - 313 7 37 55 129 25 15 1 269 2 56 52 118 29 11 - 268 10 36 57 113 25 18 1 260 7 42 56 122 27 13 - 267 3 35 48 130 32 13 1 262 6 36 44 127 26 14 1 254 3 36 61 101 26 9 - 236 7 37 55 116 29 13 1 258 11 50 46 140 29 25 2 303 6 38 51 98 24 14 2 233 6 40 43 95 32 20 1 237 7 54 40 131 36 14 2 284 4 35 55 117 35 17 - 263 7 47 47 81 33 21 - 236 4 46 44 123 18 16 - 251 9 37 57 104 28 19 1 255 8 45 38 99 28 17 - 235 7 37 48 133 22 29 1 277 6 38 33 88 26 16 - 207 9 43 66 112 30 14 - 274 2 46 47 137 24 17 - 273 8 34 60 103 28 23 2 258 9 34 45 116 24 15 3 246 7 32 52 103 22 12 - 228 7 31 36 113 23 15 2 227 2 50 50 134 30 14 1 281 13 34 62 110 26 14 1 260 6 26 36 84 28 17 1 198 4 42 54 110 30 12 - 252 6 40 45 100 26 14 1 232 2 36 46 76 17 13 - 190 5 48 71 123 33 24 - 304 3 33 40 88 29 21 - 214 7 37 42 77 21 10 1 195 4 37 37 76 11 17 1 183 3 24 37 65 14 9 - 152 9 37 42 76 15 15 - 194 9 34 60 103 23 16 - 245 4 22 50 91 24 13 4 208 1 33 31 83 10 9 - 167 2 40 42 98 11 26 1 220 4 32 33 65 7 10 1 152 1 32 40 68 13 6 3 163 2 30 35 74 18 14 2 175 5 20 42 64 16 9 2 158 2 26 35 90 14 14 2 183 4 35 34 77 14 10 - 174 3 32 42 59 15 11 1 163 5 36 40 83 20 11 - 195 2 35 60 79 19 10 - 205 4 22 40 60 14 13 1 154 6 40 47 84 20 19 1 217 1 30 31 78 12 10 1 163 1 34 31 77 13 16 3 175 6 37 47 100 17 21 - 228 2 25 42 79 15 17 - 180 6 41 52 103 14 18 1 235 3 40 62 85 19 40 1 250 3 25 46 74 17 18 - 183 4 34 59 90 11 24 1 223 2 25 49 97 20 16 2 211 2 32 54 68 16 31 1 204 4 19 70 85 12 15 1 206 6 46 60 103 22 18 1 256 3 27 43 80 15 24 - 192 4 48 52 92 21 21 - 238 7 49 61 100 12 19 - 248 - 43 53 84 12 18 1 211 5 55 63 94 17 27 - 261 4 52 55 80 17 15 1 224 4 40 35 89 17 12 - 197 7 54 68 124 18 26 - 297 3 47 58 99 10 24 - 241 2 52 43 100 10 35 - 242 3 38 45 79 9 21 1 196 2 53 56 96 11 30 1 249 5 43 51 100 18 32 1 250 7 56 55 115 15 55 1 304 3 42 48 74 15 39 - 221 2 50 49 77 18 48 1 245 12 52 58 108 22 50 3 305 4 38 43 64 13 52 1 215 9 48 45 83 14 51 1 251 6 57 68 103 20 47 2 303 12 50 63 96 24 45 - 290 9 47 76 106 16 41 1 296 8 59 109 122 30 49 1 378 8 72 95 105 21 59 - 360 5 79 86 112 23 51 2 358 9 74 68 105 21 62 1 340 4 64 57 96 20 35 - 276 10 77 79 144 27 74 - 411 8 67 70 113 16 67 2 343 5 61 62 112 28 48 3 319 6 49 65 113 25 56 1 315 12 57 62 126 43 54 1 355 17 43 77 116 30 70 1 354 22 96 94 156 42 74 - 484 7 60 76 91 23 60 - 317 10 65 65 108 19 60 - 327 19 67 71 151 19 77 2 406 7 44 52 132 36 55 2 328 11 71 78 120 20 64 1 365 17 43 67 160 27 55 2 371 9 57 61 118 24 49 1 319 15 59 94 155 23 62 2 410 18 45 77 122 26 54 3 345 9 47 86 123 31 59 3 358 4 46 72 146 51 73 1 393 12 58 102 156 39 72 - 439 16 52 57 127 32 58 - 342 9 46 94 128 30 59 1 367 7 51 97 147 46 62 2 412 11 50 67 128 31 50 4 341 14 51 70 139 31 51 4 360 4 76 74 130 40 74 1 399 8 47 59 118 29 49 4 314 12 47 65 147 43 68 1 383 13 53 71 98 17 62 2 316 15 45 63 141 53 41 2 360 9 39 58 138 47 60 1 352 7 44 72 130 49 53 - 355 16 39 53 145 44 44 1 342 9 48 73 123 48 50 4 355 11 42 62 116 51 48 2 332 4 30 77 90 47 47 1 296 10 37 98 110 44 50 4 353 12 30 70 102 33 41 3 291 8 36 56 109 47 46 3 305 9 37 67 111 41 40 2 307 12 30 33 93 36 47 2 253 3 50 48 120 46 41 5 313 4 29 52 81 24 33 1 224 9 25 69 84 17 29 5 238 5 34 55 87 22 33 1 237 4 42 52 110 30 35 3 276 4 26 43 90 24 39 1 227 2 28 49 108 18 40 5 250 7 25 68 101 24 45 3 273 4 39 59 87 24 34 2 249 13 51 48 118 28 50 5 313 6 43 50 82 19 38 1 239 7 27 44 86 22 27 1 214 3 31 57 103 26 31 3 254 10 22 50 93 14 35 1 225 5 19 52 83 16 33 5 213 5 38 74 91 21 25 3 257 3 24 35 69 15 22 1 169 4 35 37 95 23 48 - 242 4 29 53 96 29 33 4 248 9 30 45 105 17 37 - 243 5 34 62 121 24 30 2 278 7 28 57 101 18 51 5 267 6 55 68 111 22 52 4 318 8 46 69 130 8 49 3 313 7 67 83 134 26 43 9 369 4 71 81 161 22 51 3 393 4 113 95 186 34 63 1 496 5 62 89 244 25 65 2 492 12 43 84 145 20 63 2 369 11 105 87 170 35 93 15 516 11 63 100 164 38 78 5 459 11 67 63 124 24 62 5 356 15 73 94 154 14 99 - 449 17 60 95 126 25 67 6 396 11 58 95 173 31 74 8 450 12 71 98 140 17 59 6 403 10 65 83 142 32 62 3 397 13 65 121 129 27 54 5 414 13 77 148 171 30 58 5 502 14 65 93 107 19 58 - 356 3 41 79 98 36 34 1 292 7 105 129 185 27 57 13 523 4 54 85 142 28 47 - 360 9 63 86 135 38 36 2 369 10 52 88 126 26 45 3 350 7 106 92 126 25 39 11 406 13 76 86 134 30 57 9 405 12 54 69 119 27 33 2 316 15 61 103 134 30 45 4 392 14 51 112 133 50 49 - 409 12 65 87 129 48 62 6 409 5 65 82 96 34 42 1 325 5 71 82 150 41 57 6 412 6 31 71 113 26 42 3 292 7 52 64 99 39 49 3 313 9 53 97 164 33 36 12 404 2 80 130 127 38 34 7 418 6 71 114 148 28 49 2 418 21 82 117 166 36 61 2 485 16 53 89 115 20 78 3 374 14 73 121 199 39 60 7 513 12 66 142 141 22 97 12 492 8 53 117 149 32 87 7 453 5 82 143 131 32 77 6 476 19 100 131 171 31 66 12 530 9 74 125 178 28 52 5 471 12 73 147 181 44 91 8 556 12 64 107 129 40 55 3 410
Names of X columns:
Landbouw Industrie Bouw Handel Horeca Financiƫle_dienstverlening gezondheidszorg totaal_aantal_faillissementen
Sample Range:
(leave blank to include all observations)
From:
To:
Column Number of Endogenous Series
(?)
Fixed Seasonal Effects
Do not include Seasonal Dummies
Do not include Seasonal Dummies
Include Seasonal Dummies
Type of Equation
No Linear Trend
No Linear Trend
Linear Trend
First Differences
Seasonal Differences (s)
First and Seasonal Differences (s)
Degree of Predetermination (lagged endogenous variables)
Degree of Seasonal Predetermination
Seasonality
12
1
2
3
4
5
6
7
8
9
10
11
12
Chart options
R Code
library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- 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'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } 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[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) 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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) 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, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) 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,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) 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,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) 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,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) 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,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) 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') }
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