R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
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> x <- array(list(21454
+ ,-11.5
+ ,0.012095933
+ ,8.02
+ ,8.3
+ ,20780
+ ,23899
+ ,-11
+ ,0.017384968
+ ,8.03
+ ,8.2
+ ,19815
+ ,24939
+ ,-14.9
+ ,0.017547503
+ ,8.45
+ ,8
+ ,19761
+ ,23580
+ ,-16.2
+ ,0.014844804
+ ,7.74
+ ,7.9
+ ,21454
+ ,24562
+ ,-14.4
+ ,0.010364842
+ ,7.26
+ ,7.6
+ ,23899
+ ,24696
+ ,-17.3
+ ,0.016214531
+ ,7.9
+ ,7.6
+ ,24939
+ ,23785
+ ,-15.7
+ ,0.014814047
+ ,7.34
+ ,8.3
+ ,23580
+ ,23812
+ ,-12.6
+ ,0.017823834
+ ,6.91
+ ,8.4
+ ,24562
+ ,21917
+ ,-9.4
+ ,0.017980779
+ ,7.22
+ ,8.4
+ ,24696
+ ,19713
+ ,-8.1
+ ,0.015828678
+ ,7.47
+ ,8.4
+ ,23785
+ ,19282
+ ,-5.4
+ ,0.018533858
+ ,7.16
+ ,8.4
+ ,23812
+ ,18788
+ ,-4.6
+ ,0.017385905
+ ,8.09
+ ,8.6
+ ,21917
+ ,21453
+ ,-4.9
+ ,0.015866474
+ ,7.91
+ ,8.9
+ ,19713
+ ,24482
+ ,-4
+ ,0.012585695
+ ,7.74
+ ,8.8
+ ,19282
+ ,27474
+ ,-3.1
+ ,0.011326531
+ ,8.01
+ ,8.3
+ ,18788
+ ,27264
+ ,-1.3
+ ,0.019230769
+ ,7.56
+ ,7.5
+ ,21453
+ ,27349
+ ,0
+ ,0.026056627
+ ,7.56
+ ,7.2
+ ,24482
+ ,30632
+ ,-0.4
+ ,0.022604071
+ ,8.06
+ ,7.4
+ ,27474
+ ,29429
+ ,3
+ ,0.024091466
+ ,8.06
+ ,8.8
+ ,27264
+ ,30084
+ ,0.4
+ ,0.022602321
+ ,7.87
+ ,9.3
+ ,27349
+ ,26290
+ ,1.2
+ ,0.020302507
+ ,7.97
+ ,9.3
+ ,30632
+ ,24379
+ ,0.6
+ ,0.028617986
+ ,7.89
+ ,8.7
+ ,29429
+ ,23335
+ ,-1.3
+ ,0.025515909
+ ,7.83
+ ,8.2
+ ,30084
+ ,21346
+ ,-3.2
+ ,0.022785068
+ ,8.17
+ ,8.3
+ ,26290
+ ,21106
+ ,-1.8
+ ,0.022515213
+ ,8.84
+ ,8.5
+ ,24379
+ ,24514
+ ,-3.6
+ ,0.025666936
+ ,8.44
+ ,8.6
+ ,23335
+ ,28353
+ ,-4.2
+ ,0.03067299
+ ,8.38
+ ,8.5
+ ,21346
+ ,30805
+ ,-6.9
+ ,0.027599358
+ ,7.71
+ ,8.2
+ ,21106
+ ,31348
+ ,-8
+ ,0.025194961
+ ,6.58
+ ,8.1
+ ,24514
+ ,34556
+ ,-7.5
+ ,0.028705741
+ ,6.65
+ ,7.9
+ ,28353
+ ,33855
+ ,-8.2
+ ,0.031399522
+ ,6.59
+ ,8.6
+ ,30805
+ ,34787
+ ,-7.6
+ ,0.031063321
+ ,6.38
+ ,8.7
+ ,31348
+ ,32529
+ ,-3.7
+ ,0.031638643
+ ,6.78
+ ,8.7
+ ,34556
+ ,29998
+ ,-1.7
+ ,0.024653465
+ ,6.46
+ ,8.5
+ ,33855
+ ,29257
+ ,-0.7
+ ,0.025674068
+ ,6.61
+ ,8.4
+ ,34787
+ ,28155
+ ,0.2
+ ,0.028841372
+ ,6.46
+ ,8.5
+ ,32529
+ ,30466
+ ,0.6
+ ,0.026383654
+ ,6.58
+ ,8.7
+ ,29998
+ ,35704
+ ,2.2
+ ,0.023940887
+ ,6.48
+ ,8.7
+ ,29257
+ ,39327
+ ,3.3
+ ,0.017033774
+ ,6.67
+ ,8.6
+ ,28155
+ ,39351
+ ,5.3
+ ,0.019630823
+ ,6.7
+ ,8.5
+ ,30466
+ ,42234
+ ,5.5
+ ,0.021942657
+ ,6.58
+ ,8.3
+ ,35704
+ ,43630
+ ,6.3
+ ,0.018667963
+ ,6.47
+ ,8
+ ,39327
+ ,43722
+ ,7.7
+ ,0.016043298
+ ,7.25
+ ,8.2
+ ,39351
+ ,43121
+ ,6.5
+ ,0.016415604
+ ,7.24
+ ,8.1
+ ,42234
+ ,37985
+ ,5.5
+ ,0.012248047
+ ,6.97
+ ,8.1
+ ,43630
+ ,37135
+ ,6.9
+ ,0.012175089
+ ,6.83
+ ,8
+ ,43722
+ ,34646
+ ,5.7
+ ,0.014883541
+ ,7.42
+ ,7.9
+ ,43121
+ ,33026
+ ,6.9
+ ,0.016433059
+ ,7.34
+ ,7.9
+ ,37985
+ ,35087
+ ,6.1
+ ,0.016621569
+ ,7.11
+ ,8
+ ,37135
+ ,38846
+ ,4.8
+ ,0.017704224
+ ,7.16
+ ,8
+ ,34646
+ ,42013
+ ,3.7
+ ,0.018192319
+ ,7.51
+ ,7.9
+ ,33026
+ ,43908
+ ,5.8
+ ,0.017816092
+ ,7.07
+ ,8
+ ,35087
+ ,42868
+ ,6.8
+ ,0.01278748
+ ,6.85
+ ,7.7
+ ,38846
+ ,44423
+ ,8.5
+ ,0.012885368
+ ,7.05
+ ,7.2
+ ,42013
+ ,44167
+ ,7.2
+ ,0.013697327
+ ,7.62
+ ,7.5
+ ,43908
+ ,43636
+ ,5
+ ,0.011210336
+ ,7.66
+ ,7.3
+ ,42868
+ ,44382
+ ,4.7
+ ,0.015053354
+ ,7.2
+ ,7
+ ,44423
+ ,42142
+ ,2.3
+ ,0.022434368
+ ,7.38
+ ,7
+ ,44167
+ ,43452
+ ,2.4
+ ,0.029425769
+ ,7.57
+ ,7
+ ,43636
+ ,36912
+ ,0.1
+ ,0.030908226
+ ,7.31
+ ,7.2
+ ,44382
+ ,42413
+ ,1.9
+ ,0.03460076
+ ,8.33
+ ,7.3
+ ,42142
+ ,45344
+ ,1.7
+ ,0.036399735
+ ,7.38
+ ,7.1
+ ,43452
+ ,44873
+ ,2
+ ,0.043864625
+ ,7.41
+ ,6.8
+ ,36912
+ ,47510
+ ,-1.9
+ ,0.041501976
+ ,7.81
+ ,6.4
+ ,42413
+ ,49554
+ ,0.5
+ ,0.052105908
+ ,7.24
+ ,6.1
+ ,45344
+ ,47369
+ ,-1.3
+ ,0.058047493
+ ,7.88
+ ,6.5
+ ,44873
+ ,45998
+ ,-3.3
+ ,0.059116074
+ ,8.52
+ ,7.7
+ ,47510
+ ,48140
+ ,-2.8
+ ,0.053927095
+ ,7.66
+ ,7.9
+ ,49554
+ ,48441
+ ,-8
+ ,0.05462737
+ ,8.5
+ ,7.5
+ ,47369
+ ,44928
+ ,-13.9
+ ,0.047245565
+ ,8.82
+ ,6.9
+ ,45998
+ ,40454
+ ,-21.9
+ ,0.031359852
+ ,8.61
+ ,6.6
+ ,48140
+ ,38661
+ ,-28.8
+ ,0.026291513
+ ,8.2
+ ,6.9
+ ,48441
+ ,37246
+ ,-27.6
+ ,0.023153252
+ ,7.31
+ ,7.7
+ ,44928
+ ,36843
+ ,-31.4
+ ,0.019339537
+ ,7.43
+ ,8
+ ,40454
+ ,36424
+ ,-31.8
+ ,0.006158305
+ ,7.33
+ ,8
+ ,38661
+ ,37594
+ ,-29.4
+ ,0.005963676
+ ,7.53
+ ,7.7
+ ,37246
+ ,38144
+ ,-27.6
+ ,-0.003671861
+ ,7.61
+ ,7.3
+ ,36843
+ ,38737
+ ,-23.6
+ ,-0.011043819
+ ,7.17
+ ,7.4
+ ,36424
+ ,34560
+ ,-22.8
+ ,-0.016833525
+ ,6.81
+ ,8.1
+ ,37594
+ ,36080
+ ,-18.2
+ ,-0.007755393
+ ,6.9
+ ,8.3
+ ,38144
+ ,33508
+ ,-17.8
+ ,-0.011925952
+ ,7.33
+ ,8.1
+ ,38737
+ ,35462
+ ,-14.2
+ ,-0.00971826
+ ,7.36
+ ,7.9
+ ,34560
+ ,33374
+ ,-8.8
+ ,-0.001166024
+ ,6.33
+ ,7.9
+ ,36080
+ ,32110
+ ,-7.9
+ ,0.002606742
+ ,6.95
+ ,8.3
+ ,33508
+ ,35533
+ ,-7
+ ,0.006196121
+ ,7.25
+ ,8.6
+ ,35462
+ ,35532
+ ,-7
+ ,0.00698049
+ ,6.46
+ ,8.7
+ ,33374
+ ,37903
+ ,-3.6
+ ,0.016561656
+ ,6.51
+ ,8.5
+ ,32110
+ ,36763
+ ,-2.4
+ ,0.01796461
+ ,6.31
+ ,8.3
+ ,35533
+ ,40399
+ ,-4.9
+ ,0.022741573
+ ,5.93
+ ,8
+ ,35532
+ ,44164
+ ,-7.7
+ ,0.024585735
+ ,5.86
+ ,8.1
+ ,37903
+ ,44496
+ ,-6.5
+ ,0.025682617
+ ,5.85
+ ,8.9
+ ,36763
+ ,43110
+ ,-5.1
+ ,0.02317851
+ ,5.82
+ ,8.9
+ ,40399
+ ,43880
+ ,-3.4
+ ,0.029093857
+ ,6.17
+ ,8.7
+ ,44164
+ ,43930
+ ,-2.8
+ ,0.030071126
+ ,5.7
+ ,8.3
+ ,44496)
+ ,dim=c(6
+ ,94)
+ ,dimnames=list(c('Vacatures'
+ ,'Ondernemersvertrouwen'
+ ,'Inflatie'
+ ,'Rente'
+ ,'Werkloosheidsgraad'
+ ,'Vacatures_3m')
+ ,1:94))
> y <- array(NA,dim=c(6,94),dimnames=list(c('Vacatures','Ondernemersvertrouwen','Inflatie','Rente','Werkloosheidsgraad','Vacatures_3m'),1:94))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> 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
Vacatures Ondernemersvertrouwen Inflatie Rente Werkloosheidsgraad
1 21454 -11.5 0.012095933 8.02 8.3
2 23899 -11.0 0.017384968 8.03 8.2
3 24939 -14.9 0.017547503 8.45 8.0
4 23580 -16.2 0.014844804 7.74 7.9
5 24562 -14.4 0.010364842 7.26 7.6
6 24696 -17.3 0.016214531 7.90 7.6
7 23785 -15.7 0.014814047 7.34 8.3
8 23812 -12.6 0.017823834 6.91 8.4
9 21917 -9.4 0.017980779 7.22 8.4
10 19713 -8.1 0.015828678 7.47 8.4
11 19282 -5.4 0.018533858 7.16 8.4
12 18788 -4.6 0.017385905 8.09 8.6
13 21453 -4.9 0.015866474 7.91 8.9
14 24482 -4.0 0.012585695 7.74 8.8
15 27474 -3.1 0.011326531 8.01 8.3
16 27264 -1.3 0.019230769 7.56 7.5
17 27349 0.0 0.026056627 7.56 7.2
18 30632 -0.4 0.022604071 8.06 7.4
19 29429 3.0 0.024091466 8.06 8.8
20 30084 0.4 0.022602321 7.87 9.3
21 26290 1.2 0.020302507 7.97 9.3
22 24379 0.6 0.028617986 7.89 8.7
23 23335 -1.3 0.025515909 7.83 8.2
24 21346 -3.2 0.022785068 8.17 8.3
25 21106 -1.8 0.022515213 8.84 8.5
26 24514 -3.6 0.025666936 8.44 8.6
27 28353 -4.2 0.030672990 8.38 8.5
28 30805 -6.9 0.027599358 7.71 8.2
29 31348 -8.0 0.025194961 6.58 8.1
30 34556 -7.5 0.028705741 6.65 7.9
31 33855 -8.2 0.031399522 6.59 8.6
32 34787 -7.6 0.031063321 6.38 8.7
33 32529 -3.7 0.031638643 6.78 8.7
34 29998 -1.7 0.024653465 6.46 8.5
35 29257 -0.7 0.025674068 6.61 8.4
36 28155 0.2 0.028841372 6.46 8.5
37 30466 0.6 0.026383654 6.58 8.7
38 35704 2.2 0.023940887 6.48 8.7
39 39327 3.3 0.017033774 6.67 8.6
40 39351 5.3 0.019630823 6.70 8.5
41 42234 5.5 0.021942657 6.58 8.3
42 43630 6.3 0.018667963 6.47 8.0
43 43722 7.7 0.016043298 7.25 8.2
44 43121 6.5 0.016415604 7.24 8.1
45 37985 5.5 0.012248047 6.97 8.1
46 37135 6.9 0.012175089 6.83 8.0
47 34646 5.7 0.014883541 7.42 7.9
48 33026 6.9 0.016433059 7.34 7.9
49 35087 6.1 0.016621569 7.11 8.0
50 38846 4.8 0.017704224 7.16 8.0
51 42013 3.7 0.018192319 7.51 7.9
52 43908 5.8 0.017816092 7.07 8.0
53 42868 6.8 0.012787480 6.85 7.7
54 44423 8.5 0.012885368 7.05 7.2
55 44167 7.2 0.013697327 7.62 7.5
56 43636 5.0 0.011210336 7.66 7.3
57 44382 4.7 0.015053354 7.20 7.0
58 42142 2.3 0.022434368 7.38 7.0
59 43452 2.4 0.029425769 7.57 7.0
60 36912 0.1 0.030908226 7.31 7.2
61 42413 1.9 0.034600760 8.33 7.3
62 45344 1.7 0.036399735 7.38 7.1
63 44873 2.0 0.043864625 7.41 6.8
64 47510 -1.9 0.041501976 7.81 6.4
65 49554 0.5 0.052105908 7.24 6.1
66 47369 -1.3 0.058047493 7.88 6.5
67 45998 -3.3 0.059116074 8.52 7.7
68 48140 -2.8 0.053927095 7.66 7.9
69 48441 -8.0 0.054627370 8.50 7.5
70 44928 -13.9 0.047245565 8.82 6.9
71 40454 -21.9 0.031359852 8.61 6.6
72 38661 -28.8 0.026291513 8.20 6.9
73 37246 -27.6 0.023153252 7.31 7.7
74 36843 -31.4 0.019339537 7.43 8.0
75 36424 -31.8 0.006158305 7.33 8.0
76 37594 -29.4 0.005963676 7.53 7.7
77 38144 -27.6 -0.003671861 7.61 7.3
78 38737 -23.6 -0.011043819 7.17 7.4
79 34560 -22.8 -0.016833525 6.81 8.1
80 36080 -18.2 -0.007755393 6.90 8.3
81 33508 -17.8 -0.011925952 7.33 8.1
82 35462 -14.2 -0.009718260 7.36 7.9
83 33374 -8.8 -0.001166024 6.33 7.9
84 32110 -7.9 0.002606742 6.95 8.3
85 35533 -7.0 0.006196121 7.25 8.6
86 35532 -7.0 0.006980490 6.46 8.7
87 37903 -3.6 0.016561656 6.51 8.5
88 36763 -2.4 0.017964610 6.31 8.3
89 40399 -4.9 0.022741573 5.93 8.0
90 44164 -7.7 0.024585735 5.86 8.1
91 44496 -6.5 0.025682617 5.85 8.9
92 43110 -5.1 0.023178510 5.82 8.9
93 43880 -3.4 0.029093857 6.17 8.7
94 43930 -2.8 0.030071126 5.70 8.3
Vacatures_3m M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 20780 1 0 0 0 0 0 0 0 0 0 0 1
2 19815 0 1 0 0 0 0 0 0 0 0 0 2
3 19761 0 0 1 0 0 0 0 0 0 0 0 3
4 21454 0 0 0 1 0 0 0 0 0 0 0 4
5 23899 0 0 0 0 1 0 0 0 0 0 0 5
6 24939 0 0 0 0 0 1 0 0 0 0 0 6
7 23580 0 0 0 0 0 0 1 0 0 0 0 7
8 24562 0 0 0 0 0 0 0 1 0 0 0 8
9 24696 0 0 0 0 0 0 0 0 1 0 0 9
10 23785 0 0 0 0 0 0 0 0 0 1 0 10
11 23812 0 0 0 0 0 0 0 0 0 0 1 11
12 21917 0 0 0 0 0 0 0 0 0 0 0 12
13 19713 1 0 0 0 0 0 0 0 0 0 0 13
14 19282 0 1 0 0 0 0 0 0 0 0 0 14
15 18788 0 0 1 0 0 0 0 0 0 0 0 15
16 21453 0 0 0 1 0 0 0 0 0 0 0 16
17 24482 0 0 0 0 1 0 0 0 0 0 0 17
18 27474 0 0 0 0 0 1 0 0 0 0 0 18
19 27264 0 0 0 0 0 0 1 0 0 0 0 19
20 27349 0 0 0 0 0 0 0 1 0 0 0 20
21 30632 0 0 0 0 0 0 0 0 1 0 0 21
22 29429 0 0 0 0 0 0 0 0 0 1 0 22
23 30084 0 0 0 0 0 0 0 0 0 0 1 23
24 26290 0 0 0 0 0 0 0 0 0 0 0 24
25 24379 1 0 0 0 0 0 0 0 0 0 0 25
26 23335 0 1 0 0 0 0 0 0 0 0 0 26
27 21346 0 0 1 0 0 0 0 0 0 0 0 27
28 21106 0 0 0 1 0 0 0 0 0 0 0 28
29 24514 0 0 0 0 1 0 0 0 0 0 0 29
30 28353 0 0 0 0 0 1 0 0 0 0 0 30
31 30805 0 0 0 0 0 0 1 0 0 0 0 31
32 31348 0 0 0 0 0 0 0 1 0 0 0 32
33 34556 0 0 0 0 0 0 0 0 1 0 0 33
34 33855 0 0 0 0 0 0 0 0 0 1 0 34
35 34787 0 0 0 0 0 0 0 0 0 0 1 35
36 32529 0 0 0 0 0 0 0 0 0 0 0 36
37 29998 1 0 0 0 0 0 0 0 0 0 0 37
38 29257 0 1 0 0 0 0 0 0 0 0 0 38
39 28155 0 0 1 0 0 0 0 0 0 0 0 39
40 30466 0 0 0 1 0 0 0 0 0 0 0 40
41 35704 0 0 0 0 1 0 0 0 0 0 0 41
42 39327 0 0 0 0 0 1 0 0 0 0 0 42
43 39351 0 0 0 0 0 0 1 0 0 0 0 43
44 42234 0 0 0 0 0 0 0 1 0 0 0 44
45 43630 0 0 0 0 0 0 0 0 1 0 0 45
46 43722 0 0 0 0 0 0 0 0 0 1 0 46
47 43121 0 0 0 0 0 0 0 0 0 0 1 47
48 37985 0 0 0 0 0 0 0 0 0 0 0 48
49 37135 1 0 0 0 0 0 0 0 0 0 0 49
50 34646 0 1 0 0 0 0 0 0 0 0 0 50
51 33026 0 0 1 0 0 0 0 0 0 0 0 51
52 35087 0 0 0 1 0 0 0 0 0 0 0 52
53 38846 0 0 0 0 1 0 0 0 0 0 0 53
54 42013 0 0 0 0 0 1 0 0 0 0 0 54
55 43908 0 0 0 0 0 0 1 0 0 0 0 55
56 42868 0 0 0 0 0 0 0 1 0 0 0 56
57 44423 0 0 0 0 0 0 0 0 1 0 0 57
58 44167 0 0 0 0 0 0 0 0 0 1 0 58
59 43636 0 0 0 0 0 0 0 0 0 0 1 59
60 44382 0 0 0 0 0 0 0 0 0 0 0 60
61 42142 1 0 0 0 0 0 0 0 0 0 0 61
62 43452 0 1 0 0 0 0 0 0 0 0 0 62
63 36912 0 0 1 0 0 0 0 0 0 0 0 63
64 42413 0 0 0 1 0 0 0 0 0 0 0 64
65 45344 0 0 0 0 1 0 0 0 0 0 0 65
66 44873 0 0 0 0 0 1 0 0 0 0 0 66
67 47510 0 0 0 0 0 0 1 0 0 0 0 67
68 49554 0 0 0 0 0 0 0 1 0 0 0 68
69 47369 0 0 0 0 0 0 0 0 1 0 0 69
70 45998 0 0 0 0 0 0 0 0 0 1 0 70
71 48140 0 0 0 0 0 0 0 0 0 0 1 71
72 48441 0 0 0 0 0 0 0 0 0 0 0 72
73 44928 1 0 0 0 0 0 0 0 0 0 0 73
74 40454 0 1 0 0 0 0 0 0 0 0 0 74
75 38661 0 0 1 0 0 0 0 0 0 0 0 75
76 37246 0 0 0 1 0 0 0 0 0 0 0 76
77 36843 0 0 0 0 1 0 0 0 0 0 0 77
78 36424 0 0 0 0 0 1 0 0 0 0 0 78
79 37594 0 0 0 0 0 0 1 0 0 0 0 79
80 38144 0 0 0 0 0 0 0 1 0 0 0 80
81 38737 0 0 0 0 0 0 0 0 1 0 0 81
82 34560 0 0 0 0 0 0 0 0 0 1 0 82
83 36080 0 0 0 0 0 0 0 0 0 0 1 83
84 33508 0 0 0 0 0 0 0 0 0 0 0 84
85 35462 1 0 0 0 0 0 0 0 0 0 0 85
86 33374 0 1 0 0 0 0 0 0 0 0 0 86
87 32110 0 0 1 0 0 0 0 0 0 0 0 87
88 35533 0 0 0 1 0 0 0 0 0 0 0 88
89 35532 0 0 0 0 1 0 0 0 0 0 0 89
90 37903 0 0 0 0 0 1 0 0 0 0 0 90
91 36763 0 0 0 0 0 0 1 0 0 0 0 91
92 40399 0 0 0 0 0 0 0 1 0 0 0 92
93 44164 0 0 0 0 0 0 0 0 1 0 0 93
94 44496 0 0 0 0 0 0 0 0 0 1 0 94
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Ondernemersvertrouwen Inflatie
20606.8813 168.1582 56401.8572
Rente Werkloosheidsgraad Vacatures_3m
-621.1028 -1393.1088 0.5878
M1 M2 M3
3649.2147 6828.5671 9676.4699
M4 M5 M6
8533.4963 7432.1634 7701.3197
M7 M8 M9
7237.4984 6708.0482 3891.6514
M10 M11 t
2602.2131 460.4574 86.2937
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-5986.6 -1293.0 -334.1 1516.9 4134.7
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.061e+04 8.471e+03 2.433 0.01734 *
Ondernemersvertrouwen 1.682e+02 2.699e+01 6.231 2.37e-08 ***
Inflatie 5.640e+04 1.867e+04 3.021 0.00343 **
Rente -6.211e+02 4.501e+02 -1.380 0.17170
Werkloosheidsgraad -1.393e+03 6.352e+02 -2.193 0.03135 *
Vacatures_3m 5.878e-01 7.093e-02 8.287 3.10e-12 ***
M1 3.649e+03 1.122e+03 3.251 0.00171 **
M2 6.829e+03 1.125e+03 6.071 4.65e-08 ***
M3 9.676e+03 1.166e+03 8.295 2.99e-12 ***
M4 8.533e+03 1.171e+03 7.288 2.51e-10 ***
M5 7.432e+03 1.218e+03 6.101 4.09e-08 ***
M6 7.701e+03 1.174e+03 6.560 5.85e-09 ***
M7 7.237e+03 1.128e+03 6.416 1.08e-08 ***
M8 6.708e+03 1.155e+03 5.810 1.38e-07 ***
M9 3.892e+03 1.154e+03 3.372 0.00118 **
M10 2.602e+03 1.118e+03 2.327 0.02263 *
M11 4.605e+02 1.157e+03 0.398 0.69189
t 8.629e+01 1.802e+01 4.789 8.11e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 2144 on 76 degrees of freedom
Multiple R-squared: 0.9452, Adjusted R-squared: 0.9329
F-statistic: 77.06 on 17 and 76 DF, p-value: < 2.2e-16
> 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
+ }
[,1] [,2] [,3]
[1,] 0.016312460 0.032624920 0.98368754
[2,] 0.027105003 0.054210005 0.97289500
[3,] 0.009743081 0.019486162 0.99025692
[4,] 0.003180522 0.006361045 0.99681948
[5,] 0.017643431 0.035286862 0.98235657
[6,] 0.009363566 0.018727133 0.99063643
[7,] 0.008740921 0.017481842 0.99125908
[8,] 0.193042619 0.386085237 0.80695738
[9,] 0.156944882 0.313889764 0.84305512
[10,] 0.104117056 0.208234113 0.89588294
[11,] 0.067220853 0.134441705 0.93277915
[12,] 0.049945975 0.099891951 0.95005402
[13,] 0.034885805 0.069771611 0.96511419
[14,] 0.030238383 0.060476766 0.96976162
[15,] 0.025450015 0.050900030 0.97454998
[16,] 0.058659108 0.117318215 0.94134089
[17,] 0.086103504 0.172207008 0.91389650
[18,] 0.069807048 0.139614097 0.93019295
[19,] 0.062139538 0.124279075 0.93786046
[20,] 0.044677713 0.089355427 0.95532229
[21,] 0.052564804 0.105129609 0.94743520
[22,] 0.037280586 0.074561172 0.96271941
[23,] 0.088072604 0.176145208 0.91192740
[24,] 0.067865370 0.135730741 0.93213463
[25,] 0.067024656 0.134049311 0.93297534
[26,] 0.065107475 0.130214950 0.93489253
[27,] 0.076435516 0.152871031 0.92356448
[28,] 0.072464479 0.144928958 0.92753552
[29,] 0.122018861 0.244037722 0.87798114
[30,] 0.101070783 0.202141566 0.89892922
[31,] 0.084939901 0.169879802 0.91506010
[32,] 0.101069244 0.202138487 0.89893076
[33,] 0.077798595 0.155597189 0.92220141
[34,] 0.059315828 0.118631657 0.94068417
[35,] 0.046088306 0.092176613 0.95391169
[36,] 0.039661719 0.079323437 0.96033828
[37,] 0.090456668 0.180913336 0.90954333
[38,] 0.123961385 0.247922770 0.87603861
[39,] 0.394432155 0.788864311 0.60556784
[40,] 0.370318046 0.740636092 0.62968195
[41,] 0.354509805 0.709019610 0.64549019
[42,] 0.411180373 0.822360746 0.58881963
[43,] 0.346324866 0.692649732 0.65367513
[44,] 0.476195005 0.952390011 0.52380499
[45,] 0.728646969 0.542706062 0.27135303
[46,] 0.643193620 0.713612759 0.35680638
[47,] 0.824423762 0.351152475 0.17557624
[48,] 0.741238221 0.517523558 0.25876178
[49,] 0.930012571 0.139974857 0.06998743
[50,] 0.943480703 0.113038593 0.05651930
[51,] 0.894897782 0.210204436 0.10510222
[52,] 0.803994721 0.392010559 0.19600528
[53,] 0.787663936 0.424672129 0.21233606
> postscript(file="/var/www/rcomp/tmp/1d88k1292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2d88k1292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3o0751292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4o0751292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5o0751292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 94
Frequency = 1
1 2 3 4 5 6
2691.921376 1923.050878 689.489051 -817.295685 -1023.594872 -1301.167550
7 8 9 10 11 12
-598.469407 -1524.383574 -1122.467616 -1529.747880 -720.303847 1060.284875
13 14 15 16 17 18
1727.657079 1533.171446 1272.188253 -1590.208276 -3292.267040 -1272.368471
19 20 21 22 23 24
-679.671757 1468.265168 -1468.214293 -2722.564683 -2335.495164 -896.054012
25 26 27 28 29 30
-3273.639969 -2501.789141 -785.803920 2657.271994 1691.380640 1769.973180
31 32 33 34 35 36
908.802798 1891.708700 38.194559 -1297.298783 -802.571629 -486.903066
37 38 39 40 41 42
0.923259 2215.480024 3735.382260 2854.090412 3155.844183 1630.566728
43 44 45 46 47 48
2761.682658 944.364564 -2046.639120 -2205.147608 -2009.225618 -574.787450
49 50 51 52 53 54
-1629.279958 515.805755 1936.430410 3210.684582 536.921320 -988.943124
55 56 57 58 59 60
-1036.601994 -256.650103 1435.415342 648.122515 4032.593464 -2151.486057
61 62 63 64 65 66
1192.653169 -848.567126 -1280.068158 -339.825681 -777.369123 -2118.628612
67 68 69 70 71 72
-2316.953098 -980.280023 4134.663095 3402.105627 1417.298897 1430.958762
73 74 75 76 77 78
-1117.546865 -809.628190 -2360.227853 11.931811 1547.092722 1640.132661
79 80 81 82 83 84
-1903.510113 -1214.696188 -1248.766074 3373.911617 417.703897 1617.986949
85 86 87 88 89 90
407.311910 -2027.523647 -3207.390043 -5986.649157 -1838.007828 640.435188
91 92 93 94
2864.720913 -328.328544 277.814107 330.619196
> postscript(file="/var/www/rcomp/tmp/6g96q1292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 94
Frequency = 1
lag(myerror, k = 1) myerror
0 2691.921376 NA
1 1923.050878 2691.921376
2 689.489051 1923.050878
3 -817.295685 689.489051
4 -1023.594872 -817.295685
5 -1301.167550 -1023.594872
6 -598.469407 -1301.167550
7 -1524.383574 -598.469407
8 -1122.467616 -1524.383574
9 -1529.747880 -1122.467616
10 -720.303847 -1529.747880
11 1060.284875 -720.303847
12 1727.657079 1060.284875
13 1533.171446 1727.657079
14 1272.188253 1533.171446
15 -1590.208276 1272.188253
16 -3292.267040 -1590.208276
17 -1272.368471 -3292.267040
18 -679.671757 -1272.368471
19 1468.265168 -679.671757
20 -1468.214293 1468.265168
21 -2722.564683 -1468.214293
22 -2335.495164 -2722.564683
23 -896.054012 -2335.495164
24 -3273.639969 -896.054012
25 -2501.789141 -3273.639969
26 -785.803920 -2501.789141
27 2657.271994 -785.803920
28 1691.380640 2657.271994
29 1769.973180 1691.380640
30 908.802798 1769.973180
31 1891.708700 908.802798
32 38.194559 1891.708700
33 -1297.298783 38.194559
34 -802.571629 -1297.298783
35 -486.903066 -802.571629
36 0.923259 -486.903066
37 2215.480024 0.923259
38 3735.382260 2215.480024
39 2854.090412 3735.382260
40 3155.844183 2854.090412
41 1630.566728 3155.844183
42 2761.682658 1630.566728
43 944.364564 2761.682658
44 -2046.639120 944.364564
45 -2205.147608 -2046.639120
46 -2009.225618 -2205.147608
47 -574.787450 -2009.225618
48 -1629.279958 -574.787450
49 515.805755 -1629.279958
50 1936.430410 515.805755
51 3210.684582 1936.430410
52 536.921320 3210.684582
53 -988.943124 536.921320
54 -1036.601994 -988.943124
55 -256.650103 -1036.601994
56 1435.415342 -256.650103
57 648.122515 1435.415342
58 4032.593464 648.122515
59 -2151.486057 4032.593464
60 1192.653169 -2151.486057
61 -848.567126 1192.653169
62 -1280.068158 -848.567126
63 -339.825681 -1280.068158
64 -777.369123 -339.825681
65 -2118.628612 -777.369123
66 -2316.953098 -2118.628612
67 -980.280023 -2316.953098
68 4134.663095 -980.280023
69 3402.105627 4134.663095
70 1417.298897 3402.105627
71 1430.958762 1417.298897
72 -1117.546865 1430.958762
73 -809.628190 -1117.546865
74 -2360.227853 -809.628190
75 11.931811 -2360.227853
76 1547.092722 11.931811
77 1640.132661 1547.092722
78 -1903.510113 1640.132661
79 -1214.696188 -1903.510113
80 -1248.766074 -1214.696188
81 3373.911617 -1248.766074
82 417.703897 3373.911617
83 1617.986949 417.703897
84 407.311910 1617.986949
85 -2027.523647 407.311910
86 -3207.390043 -2027.523647
87 -5986.649157 -3207.390043
88 -1838.007828 -5986.649157
89 640.435188 -1838.007828
90 2864.720913 640.435188
91 -328.328544 2864.720913
92 277.814107 -328.328544
93 330.619196 277.814107
94 NA 330.619196
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 1923.050878 2691.921376
[2,] 689.489051 1923.050878
[3,] -817.295685 689.489051
[4,] -1023.594872 -817.295685
[5,] -1301.167550 -1023.594872
[6,] -598.469407 -1301.167550
[7,] -1524.383574 -598.469407
[8,] -1122.467616 -1524.383574
[9,] -1529.747880 -1122.467616
[10,] -720.303847 -1529.747880
[11,] 1060.284875 -720.303847
[12,] 1727.657079 1060.284875
[13,] 1533.171446 1727.657079
[14,] 1272.188253 1533.171446
[15,] -1590.208276 1272.188253
[16,] -3292.267040 -1590.208276
[17,] -1272.368471 -3292.267040
[18,] -679.671757 -1272.368471
[19,] 1468.265168 -679.671757
[20,] -1468.214293 1468.265168
[21,] -2722.564683 -1468.214293
[22,] -2335.495164 -2722.564683
[23,] -896.054012 -2335.495164
[24,] -3273.639969 -896.054012
[25,] -2501.789141 -3273.639969
[26,] -785.803920 -2501.789141
[27,] 2657.271994 -785.803920
[28,] 1691.380640 2657.271994
[29,] 1769.973180 1691.380640
[30,] 908.802798 1769.973180
[31,] 1891.708700 908.802798
[32,] 38.194559 1891.708700
[33,] -1297.298783 38.194559
[34,] -802.571629 -1297.298783
[35,] -486.903066 -802.571629
[36,] 0.923259 -486.903066
[37,] 2215.480024 0.923259
[38,] 3735.382260 2215.480024
[39,] 2854.090412 3735.382260
[40,] 3155.844183 2854.090412
[41,] 1630.566728 3155.844183
[42,] 2761.682658 1630.566728
[43,] 944.364564 2761.682658
[44,] -2046.639120 944.364564
[45,] -2205.147608 -2046.639120
[46,] -2009.225618 -2205.147608
[47,] -574.787450 -2009.225618
[48,] -1629.279958 -574.787450
[49,] 515.805755 -1629.279958
[50,] 1936.430410 515.805755
[51,] 3210.684582 1936.430410
[52,] 536.921320 3210.684582
[53,] -988.943124 536.921320
[54,] -1036.601994 -988.943124
[55,] -256.650103 -1036.601994
[56,] 1435.415342 -256.650103
[57,] 648.122515 1435.415342
[58,] 4032.593464 648.122515
[59,] -2151.486057 4032.593464
[60,] 1192.653169 -2151.486057
[61,] -848.567126 1192.653169
[62,] -1280.068158 -848.567126
[63,] -339.825681 -1280.068158
[64,] -777.369123 -339.825681
[65,] -2118.628612 -777.369123
[66,] -2316.953098 -2118.628612
[67,] -980.280023 -2316.953098
[68,] 4134.663095 -980.280023
[69,] 3402.105627 4134.663095
[70,] 1417.298897 3402.105627
[71,] 1430.958762 1417.298897
[72,] -1117.546865 1430.958762
[73,] -809.628190 -1117.546865
[74,] -2360.227853 -809.628190
[75,] 11.931811 -2360.227853
[76,] 1547.092722 11.931811
[77,] 1640.132661 1547.092722
[78,] -1903.510113 1640.132661
[79,] -1214.696188 -1903.510113
[80,] -1248.766074 -1214.696188
[81,] 3373.911617 -1248.766074
[82,] 417.703897 3373.911617
[83,] 1617.986949 417.703897
[84,] 407.311910 1617.986949
[85,] -2027.523647 407.311910
[86,] -3207.390043 -2027.523647
[87,] -5986.649157 -3207.390043
[88,] -1838.007828 -5986.649157
[89,] 640.435188 -1838.007828
[90,] 2864.720913 640.435188
[91,] -328.328544 2864.720913
[92,] 277.814107 -328.328544
[93,] 330.619196 277.814107
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 1923.050878 2691.921376
2 689.489051 1923.050878
3 -817.295685 689.489051
4 -1023.594872 -817.295685
5 -1301.167550 -1023.594872
6 -598.469407 -1301.167550
7 -1524.383574 -598.469407
8 -1122.467616 -1524.383574
9 -1529.747880 -1122.467616
10 -720.303847 -1529.747880
11 1060.284875 -720.303847
12 1727.657079 1060.284875
13 1533.171446 1727.657079
14 1272.188253 1533.171446
15 -1590.208276 1272.188253
16 -3292.267040 -1590.208276
17 -1272.368471 -3292.267040
18 -679.671757 -1272.368471
19 1468.265168 -679.671757
20 -1468.214293 1468.265168
21 -2722.564683 -1468.214293
22 -2335.495164 -2722.564683
23 -896.054012 -2335.495164
24 -3273.639969 -896.054012
25 -2501.789141 -3273.639969
26 -785.803920 -2501.789141
27 2657.271994 -785.803920
28 1691.380640 2657.271994
29 1769.973180 1691.380640
30 908.802798 1769.973180
31 1891.708700 908.802798
32 38.194559 1891.708700
33 -1297.298783 38.194559
34 -802.571629 -1297.298783
35 -486.903066 -802.571629
36 0.923259 -486.903066
37 2215.480024 0.923259
38 3735.382260 2215.480024
39 2854.090412 3735.382260
40 3155.844183 2854.090412
41 1630.566728 3155.844183
42 2761.682658 1630.566728
43 944.364564 2761.682658
44 -2046.639120 944.364564
45 -2205.147608 -2046.639120
46 -2009.225618 -2205.147608
47 -574.787450 -2009.225618
48 -1629.279958 -574.787450
49 515.805755 -1629.279958
50 1936.430410 515.805755
51 3210.684582 1936.430410
52 536.921320 3210.684582
53 -988.943124 536.921320
54 -1036.601994 -988.943124
55 -256.650103 -1036.601994
56 1435.415342 -256.650103
57 648.122515 1435.415342
58 4032.593464 648.122515
59 -2151.486057 4032.593464
60 1192.653169 -2151.486057
61 -848.567126 1192.653169
62 -1280.068158 -848.567126
63 -339.825681 -1280.068158
64 -777.369123 -339.825681
65 -2118.628612 -777.369123
66 -2316.953098 -2118.628612
67 -980.280023 -2316.953098
68 4134.663095 -980.280023
69 3402.105627 4134.663095
70 1417.298897 3402.105627
71 1430.958762 1417.298897
72 -1117.546865 1430.958762
73 -809.628190 -1117.546865
74 -2360.227853 -809.628190
75 11.931811 -2360.227853
76 1547.092722 11.931811
77 1640.132661 1547.092722
78 -1903.510113 1640.132661
79 -1214.696188 -1903.510113
80 -1248.766074 -1214.696188
81 3373.911617 -1248.766074
82 417.703897 3373.911617
83 1617.986949 417.703897
84 407.311910 1617.986949
85 -2027.523647 407.311910
86 -3207.390043 -2027.523647
87 -5986.649157 -3207.390043
88 -1838.007828 -5986.649157
89 640.435188 -1838.007828
90 2864.720913 640.435188
91 -328.328544 2864.720913
92 277.814107 -328.328544
93 330.619196 277.814107
> 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()
null device
1
> postscript(file="/var/www/rcomp/tmp/7r06t1292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8r06t1292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9r06t1292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10kanw1292877609.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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="/var/www/rcomp/tmp/11nsl21292877609.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
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="/var/www/rcomp/tmp/128ak81292877609.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="/var/www/rcomp/tmp/135k0h1292877609.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
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,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="/var/www/rcomp/tmp/1483gm1292877609.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="/var/www/rcomp/tmp/154vi51292877610.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="/var/www/rcomp/tmp/16peyb1292877610.tab")
+ }
>
> try(system("convert tmp/1d88k1292877609.ps tmp/1d88k1292877609.png",intern=TRUE))
character(0)
> try(system("convert tmp/2d88k1292877609.ps tmp/2d88k1292877609.png",intern=TRUE))
character(0)
> try(system("convert tmp/3o0751292877609.ps tmp/3o0751292877609.png",intern=TRUE))
character(0)
> try(system("convert tmp/4o0751292877609.ps tmp/4o0751292877609.png",intern=TRUE))
character(0)
> try(system("convert tmp/5o0751292877609.ps tmp/5o0751292877609.png",intern=TRUE))
character(0)
> try(system("convert tmp/6g96q1292877609.ps tmp/6g96q1292877609.png",intern=TRUE))
character(0)
> try(system("convert tmp/7r06t1292877609.ps tmp/7r06t1292877609.png",intern=TRUE))
character(0)
> try(system("convert tmp/8r06t1292877609.ps tmp/8r06t1292877609.png",intern=TRUE))
character(0)
> try(system("convert tmp/9r06t1292877609.ps tmp/9r06t1292877609.png",intern=TRUE))
character(0)
> try(system("convert tmp/10kanw1292877609.ps tmp/10kanw1292877609.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.500 1.760 5.216