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(3173.95
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+ ,12185.15
+ ,15648
+ ,5.7
+ ,1
+ ,1.5
+ ,3.49
+ ,4138.52
+ ,3970.1
+ ,3862.27
+ ,3701.61
+ ,4290.89
+ ,16775.08
+ ,12377.62
+ ,15305
+ ,6.9
+ ,-8
+ ,1.6
+ ,3.5
+ ,4199.75
+ ,4138.52
+ ,3970.1
+ ,3862.27
+ ,4443.91
+ ,17286.32
+ ,12512.89
+ ,15579
+ ,6.1
+ ,-1
+ ,1.7
+ ,3.64
+ ,4290.89
+ ,4199.75
+ ,4138.52
+ ,3970.1
+ ,4502.64
+ ,17741.23
+ ,12631.48
+ ,16348
+ ,4.8
+ ,1
+ ,1.8
+ ,3.94
+ ,4443.91
+ ,4290.89
+ ,4199.75
+ ,4138.52
+ ,4356.98
+ ,17128.37
+ ,12268.53
+ ,15928
+ ,3.7
+ ,-1
+ ,1.8
+ ,3.94
+ ,4502.64
+ ,4443.91
+ ,4290.89
+ ,4199.75
+ ,4591.27
+ ,17460.53
+ ,12754.8
+ ,16171
+ ,5.8
+ ,2
+ ,1.8
+ ,3.91
+ ,4356.98
+ ,4502.64
+ ,4443.91
+ ,4290.89
+ ,4696.96
+ ,17611.14
+ ,13407.75
+ ,15937
+ ,6.8
+ ,2
+ ,1.3
+ ,3.88
+ ,4591.27
+ ,4356.98
+ ,4502.64
+ ,4443.91
+ ,4621.4
+ ,18001.37
+ ,13480.21
+ ,15713
+ ,8.5
+ ,1
+ ,1.3
+ ,4.21
+ ,4696.96
+ ,4591.27
+ ,4356.98
+ ,4502.64
+ ,4562.84
+ ,17974.77
+ ,13673.28
+ ,15594
+ ,7.2
+ ,-1
+ ,1.4
+ ,4.39
+ ,4621.4
+ ,4696.96
+ ,4591.27
+ ,4356.98
+ ,4202.52
+ ,16460.95
+ ,13239.71
+ ,15683
+ ,5
+ ,-2
+ ,1.1
+ ,4.33
+ ,4562.84
+ ,4621.4
+ ,4696.96
+ ,4591.27
+ ,4296.49
+ ,16235.39
+ ,13557.69
+ ,16438
+ ,4.7
+ ,-2
+ ,1.5
+ ,4.27
+ ,4202.52
+ ,4562.84
+ ,4621.4
+ ,4696.96
+ ,4435.23
+ ,16903.36
+ ,13901.28
+ ,17032
+ ,2.3
+ ,-1
+ ,2.2
+ ,4.29
+ ,4296.49
+ ,4202.52
+ ,4562.84
+ ,4621.4
+ ,4105.18
+ ,15543.76
+ ,13200.58
+ ,17696
+ ,2.4
+ ,-8
+ ,2.9
+ ,4.18
+ ,4435.23
+ ,4296.49
+ ,4202.52
+ ,4562.84
+ ,4116.68
+ ,15532.18
+ ,13406.97
+ ,17745
+ ,0.1
+ ,-4
+ ,3.1
+ ,4.14
+ ,4105.18
+ ,4435.23
+ ,4296.49
+ ,4202.52
+ ,3844.49
+ ,13731.31
+ ,12538.12
+ ,19394
+ ,1.9
+ ,-6
+ ,3.5
+ ,4.23
+ ,4116.68
+ ,4105.18
+ ,4435.23
+ ,4296.49
+ ,3720.98
+ ,13547.84
+ ,12419.57
+ ,20148
+ ,1.7
+ ,-3
+ ,3.6
+ ,4.07
+ ,3844.49
+ ,4116.68
+ ,4105.18
+ ,4435.23
+ ,3674.4
+ ,12602.93
+ ,12193.88
+ ,20108
+ ,2
+ ,-3
+ ,4.4
+ ,3.74
+ ,3720.98
+ ,3844.49
+ ,4116.68
+ ,4105.18
+ ,3857.62
+ ,13357.7
+ ,12656.63
+ ,18584
+ ,-1.9
+ ,-7
+ ,4.2
+ ,3.66
+ ,3674.4
+ ,3720.98
+ ,3844.49
+ ,4116.68
+ ,3801.06
+ ,13995.33
+ ,12812.48
+ ,18441
+ ,0.5
+ ,-9
+ ,5.2
+ ,3.92
+ ,3857.62
+ ,3674.4
+ ,3720.98
+ ,3844.49
+ ,3504.37
+ ,14084.6
+ ,12056.67
+ ,18391
+ ,-1.3
+ ,-11
+ ,5.8
+ ,4.45
+ ,3801.06
+ ,3857.62
+ ,3674.4
+ ,3720.98
+ ,3032.6
+ ,13168.91
+ ,11322.38
+ ,19178
+ ,-3.3
+ ,-13
+ ,5.9
+ ,4.92
+ ,3504.37
+ ,3801.06
+ ,3857.62
+ ,3674.4
+ ,3047.03
+ ,12989.35
+ ,11530.75
+ ,18079
+ ,-2.8
+ ,-11
+ ,5.4
+ ,4.9
+ ,3032.6
+ ,3504.37
+ ,3801.06
+ ,3857.62
+ ,2962.34
+ ,12123.53
+ ,11114.08
+ ,18483
+ ,-8
+ ,-9
+ ,5.5
+ ,4.54
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,3801.06
+ ,2197.82
+ ,9117.03
+ ,9181.73
+ ,19644
+ ,-13.9
+ ,-17
+ ,4.7
+ ,4.53
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,3504.37
+ ,2014.45
+ ,8531.45
+ ,8614.55
+ ,19195
+ ,-21.9
+ ,-22
+ ,3.1
+ ,4.14
+ ,2197.82
+ ,2962.34
+ ,3047.03
+ ,3032.6
+ ,1862.83
+ ,8460.94
+ ,8595.56
+ ,19650
+ ,-28.8
+ ,-25
+ ,2.6
+ ,4.05
+ ,2014.45
+ ,2197.82
+ ,2962.34
+ ,3047.03
+ ,1905.41
+ ,8331.49
+ ,8396.2
+ ,20830
+ ,-27.6
+ ,-20
+ ,2.3
+ ,3.92
+ ,1862.83
+ ,2014.45
+ ,2197.82
+ ,2962.34
+ ,1810.99
+ ,7694.78
+ ,7690.5
+ ,23595
+ ,-31.4
+ ,-24
+ ,1.9
+ ,3.68
+ ,1905.41
+ ,1862.83
+ ,2014.45
+ ,2197.82
+ ,1670.07
+ ,7764.58
+ ,7235.47
+ ,22937
+ ,-31.8
+ ,-24
+ ,0.6
+ ,3.35
+ ,1810.99
+ ,1905.41
+ ,1862.83
+ ,2014.45
+ ,1864.44
+ ,8767.96
+ ,7992.12
+ ,21814
+ ,-29.4
+ ,-22
+ ,0.6
+ ,3.38
+ ,1670.07
+ ,1810.99
+ ,1905.41
+ ,1862.83
+ ,2052.02
+ ,9304.43
+ ,8398.37
+ ,21928
+ ,-27.6
+ ,-19
+ ,-0.4
+ ,3.44
+ ,1864.44
+ ,1670.07
+ ,1810.99
+ ,1905.41
+ ,2029.6
+ ,9810.31
+ ,8593
+ ,21777
+ ,-23.6
+ ,-18
+ ,-1.1
+ ,3.5
+ ,2052.02
+ ,1864.44
+ ,1670.07
+ ,1810.99
+ ,2070.83
+ ,9691.12
+ ,8679.75
+ ,21383
+ ,-22.8
+ ,-17
+ ,-1.7
+ ,3.54
+ ,2029.6
+ ,2052.02
+ ,1864.44
+ ,1670.07
+ ,2293.41
+ ,10430.35
+ ,9374.63
+ ,21467
+ ,-18.2
+ ,-11
+ ,-0.8
+ ,3.52
+ ,2070.83
+ ,2029.6
+ ,2052.02
+ ,1864.44
+ ,2443.27
+ ,10302.87
+ ,9634.97
+ ,22052
+ ,-17.8
+ ,-11
+ ,-1.2
+ ,3.53
+ ,2293.41
+ ,2070.83
+ ,2029.6
+ ,2052.02
+ ,2513.17
+ ,10066.24
+ ,9857.34
+ ,22680
+ ,-14.2
+ ,-12
+ ,-1
+ ,3.55
+ ,2443.27
+ ,2293.41
+ ,2070.83
+ ,2029.6
+ ,2466.92
+ ,9633.83
+ ,10238.83
+ ,24320
+ ,-8.8
+ ,-10
+ ,-0.1
+ ,3.37
+ ,2513.17
+ ,2443.27
+ ,2293.41
+ ,2070.83
+ ,2502.66
+ ,10169.02
+ ,10433.44
+ ,24977
+ ,-7.9
+ ,-15
+ ,0.3
+ ,3.36
+ ,2466.92
+ ,2513.17
+ ,2443.27
+ ,2293.41)
+ ,dim=c(12
+ ,128)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_5j'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:128))
> y <- array(NA,dim=c(12,128),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Goudprijs','Conjunct_Seizoenzuiver','Cons_vertrouw','Alg_consumptie_index_BE','Gem_rente_kasbon_5j','Y1','Y2','Y3','Y4'),1:128))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Do not include Seasonal 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
BEL_20 Nikkei DJ_Indust Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1 3173.95 16505.21 10853.87 8388 -0.1 2
2 3165.26 17135.96 10704.02 8099 -0.9 -8
3 3092.71 18033.25 11052.23 7984 0.0 0
4 3053.05 17671.00 10935.47 7786 0.1 -2
5 3181.96 17544.22 10714.03 8086 2.6 3
6 2999.93 17677.90 10394.48 9315 6.0 5
7 3249.57 18470.97 10817.90 9113 6.4 8
8 3210.52 18409.96 11251.20 9023 8.6 8
9 3030.29 18941.60 11281.26 9026 6.4 9
10 2803.47 19685.53 10539.68 9787 7.7 11
11 2767.63 19834.71 10483.39 9536 9.2 13
12 2882.60 19598.93 10947.43 9490 8.6 12
13 2863.36 17039.97 10580.27 9736 7.4 13
14 2897.06 16969.28 10582.92 9694 8.6 15
15 3012.61 16973.38 10654.41 9647 6.2 13
16 3142.95 16329.89 11014.51 9753 6.0 16
17 3032.93 16153.34 10967.87 10070 6.6 10
18 3045.78 15311.70 10433.56 10137 5.1 14
19 3110.52 14760.87 10665.78 9984 4.7 14
20 3013.24 14452.93 10666.71 9732 5.0 15
21 2987.10 13720.95 10682.74 9103 3.6 13
22 2995.55 13266.27 10777.22 9155 1.9 8
23 2833.18 12708.47 10052.60 9308 -0.1 7
24 2848.96 13411.84 10213.97 9394 -5.7 3
25 2794.83 13975.55 10546.82 9948 -5.6 3
26 2845.26 12974.89 10767.20 10177 -6.4 4
27 2915.02 12151.11 10444.50 10002 -7.7 4
28 2892.63 11576.21 10314.68 9728 -8.0 0
29 2604.42 9996.83 9042.56 10002 -11.9 -4
30 2641.65 10438.90 9220.75 10063 -15.4 -14
31 2659.81 10511.22 9721.84 10018 -15.5 -18
32 2638.53 10496.20 9978.53 9960 -13.4 -8
33 2720.25 10300.79 9923.81 10236 -10.9 -1
34 2745.88 9981.65 9892.56 10893 -10.8 1
35 2735.70 11448.79 10500.98 10756 -7.3 2
36 2811.70 11384.49 10179.35 10940 -6.5 0
37 2799.43 11717.46 10080.48 10997 -5.1 1
38 2555.28 10965.88 9492.44 10827 -5.3 0
39 2304.98 10352.27 8616.49 10166 -6.8 -1
40 2214.95 9751.20 8685.40 10186 -8.4 -3
41 2065.81 9354.01 8160.67 10457 -8.4 -3
42 1940.49 8792.50 8048.10 10368 -9.7 -3
43 2042.00 8721.14 8641.21 10244 -8.8 -4
44 1995.37 8692.94 8526.63 10511 -9.6 -8
45 1946.81 8570.73 8474.21 10812 -11.5 -9
46 1765.90 8538.47 7916.13 10738 -11.0 -13
47 1635.25 8169.75 7977.64 10171 -14.9 -18
48 1833.42 7905.84 8334.59 9721 -16.2 -11
49 1910.43 8145.82 8623.36 9897 -14.4 -9
50 1959.67 8895.71 9098.03 9828 -17.3 -10
51 1969.60 9676.31 9154.34 9924 -15.7 -13
52 2061.41 9884.59 9284.73 10371 -12.6 -11
53 2093.48 10637.44 9492.49 10846 -9.4 -5
54 2120.88 10717.13 9682.35 10413 -8.1 -15
55 2174.56 10205.29 9762.12 10709 -5.4 -6
56 2196.72 10295.98 10124.63 10662 -4.6 -6
57 2350.44 10892.76 10540.05 10570 -4.9 -3
58 2440.25 10631.92 10601.61 10297 -4.0 -1
59 2408.64 11441.08 10323.73 10635 -3.1 -3
60 2472.81 11950.95 10418.40 10872 -1.3 -4
61 2407.60 11037.54 10092.96 10296 0.0 -6
62 2454.62 11527.72 10364.91 10383 -0.4 0
63 2448.05 11383.89 10152.09 10431 3.0 -4
64 2497.84 10989.34 10032.80 10574 0.4 -2
65 2645.64 11079.42 10204.59 10653 1.2 -2
66 2756.76 11028.93 10001.60 10805 0.6 -6
67 2849.27 10973.00 10411.75 10872 -1.3 -7
68 2921.44 11068.05 10673.38 10625 -3.2 -6
69 2981.85 11394.84 10539.51 10407 -1.8 -6
70 3080.58 11545.71 10723.78 10463 -3.6 -3
71 3106.22 11809.38 10682.06 10556 -4.2 -2
72 3119.31 11395.64 10283.19 10646 -6.9 -5
73 3061.26 11082.38 10377.18 10702 -8.0 -11
74 3097.31 11402.75 10486.64 11353 -7.5 -11
75 3161.69 11716.87 10545.38 11346 -8.2 -11
76 3257.16 12204.98 10554.27 11451 -7.6 -10
77 3277.01 12986.62 10532.54 11964 -3.7 -14
78 3295.32 13392.79 10324.31 12574 -1.7 -8
79 3363.99 14368.05 10695.25 13031 -0.7 -9
80 3494.17 15650.83 10827.81 13812 0.2 -5
81 3667.03 16102.64 10872.48 14544 0.6 -1
82 3813.06 16187.64 10971.19 14931 2.2 -2
83 3917.96 16311.54 11145.65 14886 3.3 -5
84 3895.51 17232.97 11234.68 16005 5.3 -4
85 3801.06 16397.83 11333.88 17064 5.5 -6
86 3570.12 14990.31 10997.97 15168 6.3 -2
87 3701.61 15147.55 11036.89 16050 7.7 -2
88 3862.27 15786.78 11257.35 15839 6.5 -2
89 3970.10 15934.09 11533.59 15137 5.5 -2
90 4138.52 16519.44 11963.12 14954 6.9 2
91 4199.75 16101.07 12185.15 15648 5.7 1
92 4290.89 16775.08 12377.62 15305 6.9 -8
93 4443.91 17286.32 12512.89 15579 6.1 -1
94 4502.64 17741.23 12631.48 16348 4.8 1
95 4356.98 17128.37 12268.53 15928 3.7 -1
96 4591.27 17460.53 12754.80 16171 5.8 2
97 4696.96 17611.14 13407.75 15937 6.8 2
98 4621.40 18001.37 13480.21 15713 8.5 1
99 4562.84 17974.77 13673.28 15594 7.2 -1
100 4202.52 16460.95 13239.71 15683 5.0 -2
101 4296.49 16235.39 13557.69 16438 4.7 -2
102 4435.23 16903.36 13901.28 17032 2.3 -1
103 4105.18 15543.76 13200.58 17696 2.4 -8
104 4116.68 15532.18 13406.97 17745 0.1 -4
105 3844.49 13731.31 12538.12 19394 1.9 -6
106 3720.98 13547.84 12419.57 20148 1.7 -3
107 3674.40 12602.93 12193.88 20108 2.0 -3
108 3857.62 13357.70 12656.63 18584 -1.9 -7
109 3801.06 13995.33 12812.48 18441 0.5 -9
110 3504.37 14084.60 12056.67 18391 -1.3 -11
111 3032.60 13168.91 11322.38 19178 -3.3 -13
112 3047.03 12989.35 11530.75 18079 -2.8 -11
113 2962.34 12123.53 11114.08 18483 -8.0 -9
114 2197.82 9117.03 9181.73 19644 -13.9 -17
115 2014.45 8531.45 8614.55 19195 -21.9 -22
116 1862.83 8460.94 8595.56 19650 -28.8 -25
117 1905.41 8331.49 8396.20 20830 -27.6 -20
118 1810.99 7694.78 7690.50 23595 -31.4 -24
119 1670.07 7764.58 7235.47 22937 -31.8 -24
120 1864.44 8767.96 7992.12 21814 -29.4 -22
121 2052.02 9304.43 8398.37 21928 -27.6 -19
122 2029.60 9810.31 8593.00 21777 -23.6 -18
123 2070.83 9691.12 8679.75 21383 -22.8 -17
124 2293.41 10430.35 9374.63 21467 -18.2 -11
125 2443.27 10302.87 9634.97 22052 -17.8 -11
126 2513.17 10066.24 9857.34 22680 -14.2 -12
127 2466.92 9633.83 10238.83 24320 -8.8 -10
128 2502.66 10169.02 10433.44 24977 -7.9 -15
Alg_consumptie_index_BE Gem_rente_kasbon_5j Y1 Y2 Y3 Y4
1 0.8 3.11 3280.37 3288.18 3411.13 3484.74
2 0.7 3.57 3173.95 3280.37 3288.18 3411.13
3 0.7 4.04 3165.26 3173.95 3280.37 3288.18
4 0.9 4.21 3092.71 3165.26 3173.95 3280.37
5 1.2 4.36 3053.05 3092.71 3165.26 3173.95
6 1.3 4.75 3181.96 3053.05 3092.71 3165.26
7 1.5 4.43 2999.93 3181.96 3053.05 3092.71
8 1.9 4.70 3249.57 2999.93 3181.96 3053.05
9 1.8 4.81 3210.52 3249.57 2999.93 3181.96
10 1.9 5.01 3030.29 3210.52 3249.57 2999.93
11 2.2 5.00 2803.47 3030.29 3210.52 3249.57
12 2.1 4.81 2767.63 2803.47 3030.29 3210.52
13 2.2 5.11 2882.60 2767.63 2803.47 3030.29
14 2.7 5.10 2863.36 2882.60 2767.63 2803.47
15 2.8 5.11 2897.06 2863.36 2882.60 2767.63
16 2.9 5.21 3012.61 2897.06 2863.36 2882.60
17 3.4 5.21 3142.95 3012.61 2897.06 2863.36
18 3.0 5.21 3032.93 3142.95 3012.61 2897.06
19 3.1 5.06 3045.78 3032.93 3142.95 3012.61
20 2.5 4.58 3110.52 3045.78 3032.93 3142.95
21 2.2 4.37 3013.24 3110.52 3045.78 3032.93
22 2.3 4.37 2987.10 3013.24 3110.52 3045.78
23 2.1 4.23 2995.55 2987.10 3013.24 3110.52
24 2.8 4.23 2833.18 2995.55 2987.10 3013.24
25 3.1 4.37 2848.96 2833.18 2995.55 2987.10
26 2.9 4.31 2794.83 2848.96 2833.18 2995.55
27 2.6 4.31 2845.26 2794.83 2848.96 2833.18
28 2.7 4.28 2915.02 2845.26 2794.83 2848.96
29 2.3 3.98 2892.63 2915.02 2845.26 2794.83
30 2.3 3.79 2604.42 2892.63 2915.02 2845.26
31 2.1 3.55 2641.65 2604.42 2892.63 2915.02
32 2.2 4.00 2659.81 2641.65 2604.42 2892.63
33 2.9 4.02 2638.53 2659.81 2641.65 2604.42
34 2.6 4.21 2720.25 2638.53 2659.81 2641.65
35 2.7 4.50 2745.88 2720.25 2638.53 2659.81
36 1.8 4.52 2735.70 2745.88 2720.25 2638.53
37 1.3 4.45 2811.70 2735.70 2745.88 2720.25
38 0.9 4.28 2799.43 2811.70 2735.70 2745.88
39 1.3 4.08 2555.28 2799.43 2811.70 2735.70
40 1.3 3.80 2304.98 2555.28 2799.43 2811.70
41 1.3 3.58 2214.95 2304.98 2555.28 2799.43
42 1.3 3.58 2065.81 2214.95 2304.98 2555.28
43 1.1 3.58 1940.49 2065.81 2214.95 2304.98
44 1.4 3.54 2042.00 1940.49 2065.81 2214.95
45 1.2 3.19 1995.37 2042.00 1940.49 2065.81
46 1.7 2.91 1946.81 1995.37 2042.00 1940.49
47 1.8 2.87 1765.90 1946.81 1995.37 2042.00
48 1.5 3.10 1635.25 1765.90 1946.81 1995.37
49 1.0 2.60 1833.42 1635.25 1765.90 1946.81
50 1.6 2.33 1910.43 1833.42 1635.25 1765.90
51 1.5 2.62 1959.67 1910.43 1833.42 1635.25
52 1.8 3.05 1969.60 1959.67 1910.43 1833.42
53 1.8 3.05 2061.41 1969.60 1959.67 1910.43
54 1.6 3.22 2093.48 2061.41 1969.60 1959.67
55 1.9 3.24 2120.88 2093.48 2061.41 1969.60
56 1.7 3.24 2174.56 2120.88 2093.48 2061.41
57 1.6 3.38 2196.72 2174.56 2120.88 2093.48
58 1.3 3.35 2350.44 2196.72 2174.56 2120.88
59 1.1 3.22 2440.25 2350.44 2196.72 2174.56
60 1.9 3.06 2408.64 2440.25 2350.44 2196.72
61 2.6 3.17 2472.81 2408.64 2440.25 2350.44
62 2.3 3.19 2407.60 2472.81 2408.64 2440.25
63 2.4 3.35 2454.62 2407.60 2472.81 2408.64
64 2.2 3.24 2448.05 2454.62 2407.60 2472.81
65 2.0 3.23 2497.84 2448.05 2454.62 2407.60
66 2.9 3.31 2645.64 2497.84 2448.05 2454.62
67 2.6 3.25 2756.76 2645.64 2497.84 2448.05
68 2.3 3.20 2849.27 2756.76 2645.64 2497.84
69 2.3 3.10 2921.44 2849.27 2756.76 2645.64
70 2.6 2.93 2981.85 2921.44 2849.27 2756.76
71 3.1 2.92 3080.58 2981.85 2921.44 2849.27
72 2.8 2.90 3106.22 3080.58 2981.85 2921.44
73 2.5 2.87 3119.31 3106.22 3080.58 2981.85
74 2.9 2.76 3061.26 3119.31 3106.22 3080.58
75 3.1 2.67 3097.31 3061.26 3119.31 3106.22
76 3.1 2.75 3161.69 3097.31 3061.26 3119.31
77 3.2 2.72 3257.16 3161.69 3097.31 3061.26
78 2.5 2.72 3277.01 3257.16 3161.69 3097.31
79 2.6 2.86 3295.32 3277.01 3257.16 3161.69
80 2.9 2.99 3363.99 3295.32 3277.01 3257.16
81 2.6 3.07 3494.17 3363.99 3295.32 3277.01
82 2.4 2.96 3667.03 3494.17 3363.99 3295.32
83 1.7 3.04 3813.06 3667.03 3494.17 3363.99
84 2.0 3.30 3917.96 3813.06 3667.03 3494.17
85 2.2 3.48 3895.51 3917.96 3813.06 3667.03
86 1.9 3.46 3801.06 3895.51 3917.96 3813.06
87 1.6 3.57 3570.12 3801.06 3895.51 3917.96
88 1.6 3.60 3701.61 3570.12 3801.06 3895.51
89 1.2 3.51 3862.27 3701.61 3570.12 3801.06
90 1.2 3.52 3970.10 3862.27 3701.61 3570.12
91 1.5 3.49 4138.52 3970.10 3862.27 3701.61
92 1.6 3.50 4199.75 4138.52 3970.10 3862.27
93 1.7 3.64 4290.89 4199.75 4138.52 3970.10
94 1.8 3.94 4443.91 4290.89 4199.75 4138.52
95 1.8 3.94 4502.64 4443.91 4290.89 4199.75
96 1.8 3.91 4356.98 4502.64 4443.91 4290.89
97 1.3 3.88 4591.27 4356.98 4502.64 4443.91
98 1.3 4.21 4696.96 4591.27 4356.98 4502.64
99 1.4 4.39 4621.40 4696.96 4591.27 4356.98
100 1.1 4.33 4562.84 4621.40 4696.96 4591.27
101 1.5 4.27 4202.52 4562.84 4621.40 4696.96
102 2.2 4.29 4296.49 4202.52 4562.84 4621.40
103 2.9 4.18 4435.23 4296.49 4202.52 4562.84
104 3.1 4.14 4105.18 4435.23 4296.49 4202.52
105 3.5 4.23 4116.68 4105.18 4435.23 4296.49
106 3.6 4.07 3844.49 4116.68 4105.18 4435.23
107 4.4 3.74 3720.98 3844.49 4116.68 4105.18
108 4.2 3.66 3674.40 3720.98 3844.49 4116.68
109 5.2 3.92 3857.62 3674.40 3720.98 3844.49
110 5.8 4.45 3801.06 3857.62 3674.40 3720.98
111 5.9 4.92 3504.37 3801.06 3857.62 3674.40
112 5.4 4.90 3032.60 3504.37 3801.06 3857.62
113 5.5 4.54 3047.03 3032.60 3504.37 3801.06
114 4.7 4.53 2962.34 3047.03 3032.60 3504.37
115 3.1 4.14 2197.82 2962.34 3047.03 3032.60
116 2.6 4.05 2014.45 2197.82 2962.34 3047.03
117 2.3 3.92 1862.83 2014.45 2197.82 2962.34
118 1.9 3.68 1905.41 1862.83 2014.45 2197.82
119 0.6 3.35 1810.99 1905.41 1862.83 2014.45
120 0.6 3.38 1670.07 1810.99 1905.41 1862.83
121 -0.4 3.44 1864.44 1670.07 1810.99 1905.41
122 -1.1 3.50 2052.02 1864.44 1670.07 1810.99
123 -1.7 3.54 2029.60 2052.02 1864.44 1670.07
124 -0.8 3.52 2070.83 2029.60 2052.02 1864.44
125 -1.2 3.53 2293.41 2070.83 2029.60 2052.02
126 -1.0 3.55 2443.27 2293.41 2070.83 2029.60
127 -0.1 3.37 2513.17 2443.27 2293.41 2070.83
128 0.3 3.36 2466.92 2513.17 2443.27 2293.41
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-2.607e+02 2.505e-02 1.026e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
6.957e-03 -1.026e+01 1.300e+01
Alg_consumptie_index_BE Gem_rente_kasbon_5j Y1
-7.334e+00 -1.659e+02 8.068e-01
Y2 Y3 Y4
-1.144e-01 1.820e-01 -7.303e-02
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-322.23 -68.89 2.94 66.63 200.78
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.607e+02 1.579e+02 -1.651 0.101505
Nikkei 2.505e-02 7.214e-03 3.473 0.000725 ***
DJ_Indust 1.026e-01 1.948e-02 5.267 6.46e-07 ***
Goudprijs 6.957e-03 3.585e-03 1.940 0.054758 .
Conjunct_Seizoenzuiver -1.026e+01 3.081e+00 -3.331 0.001161 **
Cons_vertrouw 1.300e+01 2.937e+00 4.425 2.19e-05 ***
Alg_consumptie_index_BE -7.334e+00 1.030e+01 -0.712 0.477842
Gem_rente_kasbon_5j -1.659e+02 2.557e+01 -6.487 2.24e-09 ***
Y1 8.068e-01 8.534e-02 9.454 4.53e-16 ***
Y2 -1.144e-01 1.125e-01 -1.017 0.311225
Y3 1.820e-01 1.115e-01 1.633 0.105276
Y4 -7.303e-02 7.351e-02 -0.993 0.322547
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 110.6 on 116 degrees of freedom
Multiple R-squared: 0.9807, Adjusted R-squared: 0.9789
F-statistic: 536 on 11 and 116 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,] 0.8002349 0.399530162 0.199765081
[2,] 0.8230518 0.353896478 0.176948239
[3,] 0.7320926 0.535814701 0.267907350
[4,] 0.6455304 0.708939148 0.354469574
[5,] 0.5565252 0.886949660 0.443474830
[6,] 0.6213601 0.757279730 0.378639865
[7,] 0.8515799 0.296840231 0.148420116
[8,] 0.8495037 0.300992531 0.150496265
[9,] 0.8535315 0.292937011 0.146468506
[10,] 0.8054615 0.389076964 0.194538482
[11,] 0.7902434 0.419513128 0.209756564
[12,] 0.7383098 0.523380349 0.261690174
[13,] 0.6829169 0.634166123 0.317083062
[14,] 0.6135167 0.772966698 0.386483349
[15,] 0.5618172 0.876365566 0.438182783
[16,] 0.5442051 0.911589791 0.455794896
[17,] 0.4939166 0.987833108 0.506083446
[18,] 0.4410783 0.882156582 0.558921709
[19,] 0.4033456 0.806691208 0.596654396
[20,] 0.3739187 0.747837305 0.626081348
[21,] 0.3637778 0.727555574 0.636222213
[22,] 0.3806316 0.761263227 0.619368387
[23,] 0.3567116 0.713423103 0.643288448
[24,] 0.3756147 0.751229469 0.624385265
[25,] 0.4071528 0.814305607 0.592847197
[26,] 0.3705143 0.741028668 0.629485666
[27,] 0.3181634 0.636326836 0.681836582
[28,] 0.2808634 0.561726761 0.719136619
[29,] 0.2745249 0.549049844 0.725475078
[30,] 0.2678345 0.535668980 0.732165510
[31,] 0.2378852 0.475770451 0.762114775
[32,] 0.2297459 0.459491768 0.770254116
[33,] 0.2511732 0.502346471 0.748826764
[34,] 0.2508542 0.501708418 0.749145791
[35,] 0.2086163 0.417232696 0.791383652
[36,] 0.2124842 0.424968443 0.787515779
[37,] 0.2384027 0.476805428 0.761597286
[38,] 0.1966696 0.393339287 0.803330357
[39,] 0.2035494 0.407098833 0.796450583
[40,] 0.1799373 0.359874553 0.820062724
[41,] 0.1491970 0.298394043 0.850802979
[42,] 0.1516323 0.303264663 0.848367669
[43,] 0.1278098 0.255619616 0.872190192
[44,] 0.1105587 0.221117494 0.889441253
[45,] 0.1343609 0.268721754 0.865639123
[46,] 0.1922759 0.384551893 0.807724053
[47,] 0.2011155 0.402231064 0.798884468
[48,] 0.3304073 0.660814646 0.669592677
[49,] 0.3643645 0.728729088 0.635635456
[50,] 0.4390996 0.878199143 0.560900428
[51,] 0.5297051 0.940589808 0.470294904
[52,] 0.6786845 0.642631075 0.321315538
[53,] 0.7272300 0.545539908 0.272769954
[54,] 0.7108623 0.578275487 0.289137744
[55,] 0.7091513 0.581697421 0.290848711
[56,] 0.6876721 0.624655822 0.312327911
[57,] 0.6594780 0.681043988 0.340521994
[58,] 0.6285771 0.742845771 0.371422885
[59,] 0.5816960 0.836607936 0.418303968
[60,] 0.5313119 0.937376146 0.468688073
[61,] 0.4807103 0.961420651 0.519289675
[62,] 0.4632129 0.926425837 0.536787082
[63,] 0.4454466 0.890893115 0.554553442
[64,] 0.4141868 0.828373518 0.585813241
[65,] 0.3747359 0.749471708 0.625264146
[66,] 0.4607592 0.921518302 0.539240849
[67,] 0.5817372 0.836525698 0.418262849
[68,] 0.6441524 0.711695188 0.355847594
[69,] 0.6117084 0.776583135 0.388291568
[70,] 0.7774416 0.445116878 0.222558439
[71,] 0.9166393 0.166721433 0.083360717
[72,] 0.9956185 0.008763059 0.004381530
[73,] 0.9950731 0.009853864 0.004926932
[74,] 0.9931929 0.013614119 0.006807060
[75,] 0.9903161 0.019367803 0.009683902
[76,] 0.9896987 0.020602677 0.010301339
[77,] 0.9870089 0.025982230 0.012991115
[78,] 0.9814774 0.037045144 0.018522572
[79,] 0.9721779 0.055644111 0.027822056
[80,] 0.9601322 0.079735505 0.039867753
[81,] 0.9595908 0.080818348 0.040409174
[82,] 0.9573200 0.085359967 0.042679983
[83,] 0.9496328 0.100734471 0.050367236
[84,] 0.9511346 0.097730832 0.048865416
[85,] 0.9644376 0.071124897 0.035562449
[86,] 0.9821357 0.035728512 0.017864256
[87,] 0.9756295 0.048741065 0.024370532
[88,] 0.9616435 0.076713058 0.038356529
[89,] 0.9655769 0.068846133 0.034423066
[90,] 0.9805943 0.038811329 0.019405664
[91,] 0.9901150 0.019769927 0.009884963
[92,] 0.9903715 0.019257045 0.009628522
[93,] 0.9832363 0.033527485 0.016763742
[94,] 0.9715963 0.056807395 0.028403698
[95,] 0.9558482 0.088303645 0.044151823
[96,] 0.9176742 0.164651631 0.082325816
[97,] 0.9781753 0.043649461 0.021824730
[98,] 0.9489600 0.102080094 0.051040047
[99,] 0.9066752 0.186649574 0.093324787
> postscript(file="/var/www/rcomp/tmp/14ohz1291653832.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/24ohz1291653832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ffg11291653832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4ffg11291653832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5ffg11291653832.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 = 128
Frequency = 1
1 2 3 4 5 6
-293.253957 -1.074005 -160.533190 -44.737562 113.255503 -69.526962
7 8 9 10 11 12
195.229158 -64.431142 -176.544154 -247.710465 -102.098769 -21.019058
13 14 15 16 17 18
16.261181 58.717199 117.975666 125.547262 10.852105 113.953525
19 20 21 22 23 24
103.237437 -99.825780 -54.787083 2.379463 -92.305781 17.958437
25 26 27 28 29 30
-96.613108 -2.305786 45.277793 57.671814 -90.139606 200.782814
31 32 33 34 35 36
121.861619 82.407866 107.279705 74.937881 32.140489 167.118602
37 38 39 40 41 42
81.173656 -80.483162 -72.546246 -9.802604 -45.972297 -20.643326
43 44 45 46 47 48
125.565509 53.215355 6.127331 -95.625078 -44.049227 149.012061
49 50 51 52 53 54
-50.423594 -154.255446 -144.076045 3.449087 -129.377339 36.001401
55 56 57 58 59 60
-25.898781 -75.452107 -12.639654 -73.901004 -142.403819 -82.155362
61 62 63 64 65 66
-84.868704 -87.344513 -14.035158 11.732708 89.938203 178.873330
67 68 69 70 71 72
129.281018 45.758379 53.944640 -3.069953 -76.670487 -22.261009
73 74 75 76 77 78
-44.173613 8.783272 2.432736 54.164775 60.399095 8.899461
79 80 81 82 83 84
33.925801 43.997118 59.994020 65.042137 90.937408 -8.544849
85 86 87 88 89 90
-24.093584 -156.187989 178.912345 178.104907 151.203328 116.776380
91 92 93 94 95 96
15.592320 166.123801 126.015176 55.111316 -62.315167 192.690178
97 98 99 100 101 102
25.638622 -8.069567 -21.934543 -285.706980 71.409248 12.949109
103 104 105 106 107 108
-176.747762 -28.494910 -180.962046 -68.681138 -71.621026 126.849996
109 110 111 112 113 114
-10.153684 -65.284146 -163.914296 183.793314 6.664436 -322.231826
115 116 117 118 119 120
46.910006 -77.625507 138.325437 9.131443 -55.382808 131.694821
121 122 123 124 125 126
92.805217 -38.694710 -9.310736 39.593571 7.604312 6.380182
127 128
-150.176485 -42.604790
> postscript(file="/var/www/rcomp/tmp/686y41291653832.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 = 128
Frequency = 1
lag(myerror, k = 1) myerror
0 -293.253957 NA
1 -1.074005 -293.253957
2 -160.533190 -1.074005
3 -44.737562 -160.533190
4 113.255503 -44.737562
5 -69.526962 113.255503
6 195.229158 -69.526962
7 -64.431142 195.229158
8 -176.544154 -64.431142
9 -247.710465 -176.544154
10 -102.098769 -247.710465
11 -21.019058 -102.098769
12 16.261181 -21.019058
13 58.717199 16.261181
14 117.975666 58.717199
15 125.547262 117.975666
16 10.852105 125.547262
17 113.953525 10.852105
18 103.237437 113.953525
19 -99.825780 103.237437
20 -54.787083 -99.825780
21 2.379463 -54.787083
22 -92.305781 2.379463
23 17.958437 -92.305781
24 -96.613108 17.958437
25 -2.305786 -96.613108
26 45.277793 -2.305786
27 57.671814 45.277793
28 -90.139606 57.671814
29 200.782814 -90.139606
30 121.861619 200.782814
31 82.407866 121.861619
32 107.279705 82.407866
33 74.937881 107.279705
34 32.140489 74.937881
35 167.118602 32.140489
36 81.173656 167.118602
37 -80.483162 81.173656
38 -72.546246 -80.483162
39 -9.802604 -72.546246
40 -45.972297 -9.802604
41 -20.643326 -45.972297
42 125.565509 -20.643326
43 53.215355 125.565509
44 6.127331 53.215355
45 -95.625078 6.127331
46 -44.049227 -95.625078
47 149.012061 -44.049227
48 -50.423594 149.012061
49 -154.255446 -50.423594
50 -144.076045 -154.255446
51 3.449087 -144.076045
52 -129.377339 3.449087
53 36.001401 -129.377339
54 -25.898781 36.001401
55 -75.452107 -25.898781
56 -12.639654 -75.452107
57 -73.901004 -12.639654
58 -142.403819 -73.901004
59 -82.155362 -142.403819
60 -84.868704 -82.155362
61 -87.344513 -84.868704
62 -14.035158 -87.344513
63 11.732708 -14.035158
64 89.938203 11.732708
65 178.873330 89.938203
66 129.281018 178.873330
67 45.758379 129.281018
68 53.944640 45.758379
69 -3.069953 53.944640
70 -76.670487 -3.069953
71 -22.261009 -76.670487
72 -44.173613 -22.261009
73 8.783272 -44.173613
74 2.432736 8.783272
75 54.164775 2.432736
76 60.399095 54.164775
77 8.899461 60.399095
78 33.925801 8.899461
79 43.997118 33.925801
80 59.994020 43.997118
81 65.042137 59.994020
82 90.937408 65.042137
83 -8.544849 90.937408
84 -24.093584 -8.544849
85 -156.187989 -24.093584
86 178.912345 -156.187989
87 178.104907 178.912345
88 151.203328 178.104907
89 116.776380 151.203328
90 15.592320 116.776380
91 166.123801 15.592320
92 126.015176 166.123801
93 55.111316 126.015176
94 -62.315167 55.111316
95 192.690178 -62.315167
96 25.638622 192.690178
97 -8.069567 25.638622
98 -21.934543 -8.069567
99 -285.706980 -21.934543
100 71.409248 -285.706980
101 12.949109 71.409248
102 -176.747762 12.949109
103 -28.494910 -176.747762
104 -180.962046 -28.494910
105 -68.681138 -180.962046
106 -71.621026 -68.681138
107 126.849996 -71.621026
108 -10.153684 126.849996
109 -65.284146 -10.153684
110 -163.914296 -65.284146
111 183.793314 -163.914296
112 6.664436 183.793314
113 -322.231826 6.664436
114 46.910006 -322.231826
115 -77.625507 46.910006
116 138.325437 -77.625507
117 9.131443 138.325437
118 -55.382808 9.131443
119 131.694821 -55.382808
120 92.805217 131.694821
121 -38.694710 92.805217
122 -9.310736 -38.694710
123 39.593571 -9.310736
124 7.604312 39.593571
125 6.380182 7.604312
126 -150.176485 6.380182
127 -42.604790 -150.176485
128 NA -42.604790
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -1.074005 -293.253957
[2,] -160.533190 -1.074005
[3,] -44.737562 -160.533190
[4,] 113.255503 -44.737562
[5,] -69.526962 113.255503
[6,] 195.229158 -69.526962
[7,] -64.431142 195.229158
[8,] -176.544154 -64.431142
[9,] -247.710465 -176.544154
[10,] -102.098769 -247.710465
[11,] -21.019058 -102.098769
[12,] 16.261181 -21.019058
[13,] 58.717199 16.261181
[14,] 117.975666 58.717199
[15,] 125.547262 117.975666
[16,] 10.852105 125.547262
[17,] 113.953525 10.852105
[18,] 103.237437 113.953525
[19,] -99.825780 103.237437
[20,] -54.787083 -99.825780
[21,] 2.379463 -54.787083
[22,] -92.305781 2.379463
[23,] 17.958437 -92.305781
[24,] -96.613108 17.958437
[25,] -2.305786 -96.613108
[26,] 45.277793 -2.305786
[27,] 57.671814 45.277793
[28,] -90.139606 57.671814
[29,] 200.782814 -90.139606
[30,] 121.861619 200.782814
[31,] 82.407866 121.861619
[32,] 107.279705 82.407866
[33,] 74.937881 107.279705
[34,] 32.140489 74.937881
[35,] 167.118602 32.140489
[36,] 81.173656 167.118602
[37,] -80.483162 81.173656
[38,] -72.546246 -80.483162
[39,] -9.802604 -72.546246
[40,] -45.972297 -9.802604
[41,] -20.643326 -45.972297
[42,] 125.565509 -20.643326
[43,] 53.215355 125.565509
[44,] 6.127331 53.215355
[45,] -95.625078 6.127331
[46,] -44.049227 -95.625078
[47,] 149.012061 -44.049227
[48,] -50.423594 149.012061
[49,] -154.255446 -50.423594
[50,] -144.076045 -154.255446
[51,] 3.449087 -144.076045
[52,] -129.377339 3.449087
[53,] 36.001401 -129.377339
[54,] -25.898781 36.001401
[55,] -75.452107 -25.898781
[56,] -12.639654 -75.452107
[57,] -73.901004 -12.639654
[58,] -142.403819 -73.901004
[59,] -82.155362 -142.403819
[60,] -84.868704 -82.155362
[61,] -87.344513 -84.868704
[62,] -14.035158 -87.344513
[63,] 11.732708 -14.035158
[64,] 89.938203 11.732708
[65,] 178.873330 89.938203
[66,] 129.281018 178.873330
[67,] 45.758379 129.281018
[68,] 53.944640 45.758379
[69,] -3.069953 53.944640
[70,] -76.670487 -3.069953
[71,] -22.261009 -76.670487
[72,] -44.173613 -22.261009
[73,] 8.783272 -44.173613
[74,] 2.432736 8.783272
[75,] 54.164775 2.432736
[76,] 60.399095 54.164775
[77,] 8.899461 60.399095
[78,] 33.925801 8.899461
[79,] 43.997118 33.925801
[80,] 59.994020 43.997118
[81,] 65.042137 59.994020
[82,] 90.937408 65.042137
[83,] -8.544849 90.937408
[84,] -24.093584 -8.544849
[85,] -156.187989 -24.093584
[86,] 178.912345 -156.187989
[87,] 178.104907 178.912345
[88,] 151.203328 178.104907
[89,] 116.776380 151.203328
[90,] 15.592320 116.776380
[91,] 166.123801 15.592320
[92,] 126.015176 166.123801
[93,] 55.111316 126.015176
[94,] -62.315167 55.111316
[95,] 192.690178 -62.315167
[96,] 25.638622 192.690178
[97,] -8.069567 25.638622
[98,] -21.934543 -8.069567
[99,] -285.706980 -21.934543
[100,] 71.409248 -285.706980
[101,] 12.949109 71.409248
[102,] -176.747762 12.949109
[103,] -28.494910 -176.747762
[104,] -180.962046 -28.494910
[105,] -68.681138 -180.962046
[106,] -71.621026 -68.681138
[107,] 126.849996 -71.621026
[108,] -10.153684 126.849996
[109,] -65.284146 -10.153684
[110,] -163.914296 -65.284146
[111,] 183.793314 -163.914296
[112,] 6.664436 183.793314
[113,] -322.231826 6.664436
[114,] 46.910006 -322.231826
[115,] -77.625507 46.910006
[116,] 138.325437 -77.625507
[117,] 9.131443 138.325437
[118,] -55.382808 9.131443
[119,] 131.694821 -55.382808
[120,] 92.805217 131.694821
[121,] -38.694710 92.805217
[122,] -9.310736 -38.694710
[123,] 39.593571 -9.310736
[124,] 7.604312 39.593571
[125,] 6.380182 7.604312
[126,] -150.176485 6.380182
[127,] -42.604790 -150.176485
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -1.074005 -293.253957
2 -160.533190 -1.074005
3 -44.737562 -160.533190
4 113.255503 -44.737562
5 -69.526962 113.255503
6 195.229158 -69.526962
7 -64.431142 195.229158
8 -176.544154 -64.431142
9 -247.710465 -176.544154
10 -102.098769 -247.710465
11 -21.019058 -102.098769
12 16.261181 -21.019058
13 58.717199 16.261181
14 117.975666 58.717199
15 125.547262 117.975666
16 10.852105 125.547262
17 113.953525 10.852105
18 103.237437 113.953525
19 -99.825780 103.237437
20 -54.787083 -99.825780
21 2.379463 -54.787083
22 -92.305781 2.379463
23 17.958437 -92.305781
24 -96.613108 17.958437
25 -2.305786 -96.613108
26 45.277793 -2.305786
27 57.671814 45.277793
28 -90.139606 57.671814
29 200.782814 -90.139606
30 121.861619 200.782814
31 82.407866 121.861619
32 107.279705 82.407866
33 74.937881 107.279705
34 32.140489 74.937881
35 167.118602 32.140489
36 81.173656 167.118602
37 -80.483162 81.173656
38 -72.546246 -80.483162
39 -9.802604 -72.546246
40 -45.972297 -9.802604
41 -20.643326 -45.972297
42 125.565509 -20.643326
43 53.215355 125.565509
44 6.127331 53.215355
45 -95.625078 6.127331
46 -44.049227 -95.625078
47 149.012061 -44.049227
48 -50.423594 149.012061
49 -154.255446 -50.423594
50 -144.076045 -154.255446
51 3.449087 -144.076045
52 -129.377339 3.449087
53 36.001401 -129.377339
54 -25.898781 36.001401
55 -75.452107 -25.898781
56 -12.639654 -75.452107
57 -73.901004 -12.639654
58 -142.403819 -73.901004
59 -82.155362 -142.403819
60 -84.868704 -82.155362
61 -87.344513 -84.868704
62 -14.035158 -87.344513
63 11.732708 -14.035158
64 89.938203 11.732708
65 178.873330 89.938203
66 129.281018 178.873330
67 45.758379 129.281018
68 53.944640 45.758379
69 -3.069953 53.944640
70 -76.670487 -3.069953
71 -22.261009 -76.670487
72 -44.173613 -22.261009
73 8.783272 -44.173613
74 2.432736 8.783272
75 54.164775 2.432736
76 60.399095 54.164775
77 8.899461 60.399095
78 33.925801 8.899461
79 43.997118 33.925801
80 59.994020 43.997118
81 65.042137 59.994020
82 90.937408 65.042137
83 -8.544849 90.937408
84 -24.093584 -8.544849
85 -156.187989 -24.093584
86 178.912345 -156.187989
87 178.104907 178.912345
88 151.203328 178.104907
89 116.776380 151.203328
90 15.592320 116.776380
91 166.123801 15.592320
92 126.015176 166.123801
93 55.111316 126.015176
94 -62.315167 55.111316
95 192.690178 -62.315167
96 25.638622 192.690178
97 -8.069567 25.638622
98 -21.934543 -8.069567
99 -285.706980 -21.934543
100 71.409248 -285.706980
101 12.949109 71.409248
102 -176.747762 12.949109
103 -28.494910 -176.747762
104 -180.962046 -28.494910
105 -68.681138 -180.962046
106 -71.621026 -68.681138
107 126.849996 -71.621026
108 -10.153684 126.849996
109 -65.284146 -10.153684
110 -163.914296 -65.284146
111 183.793314 -163.914296
112 6.664436 183.793314
113 -322.231826 6.664436
114 46.910006 -322.231826
115 -77.625507 46.910006
116 138.325437 -77.625507
117 9.131443 138.325437
118 -55.382808 9.131443
119 131.694821 -55.382808
120 92.805217 131.694821
121 -38.694710 92.805217
122 -9.310736 -38.694710
123 39.593571 -9.310736
124 7.604312 39.593571
125 6.380182 7.604312
126 -150.176485 6.380182
127 -42.604790 -150.176485
> 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/70fx71291653832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/80fx71291653832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/90fx71291653832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10t7es1291653832.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11e7vy1291653832.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/12i8um1291653832.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/13e09d1291653832.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/14ziq11291653832.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/15dt921291653833.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/16gt7p1291653833.tab")
+ }
>
> try(system("convert tmp/14ohz1291653832.ps tmp/14ohz1291653832.png",intern=TRUE))
character(0)
> try(system("convert tmp/24ohz1291653832.ps tmp/24ohz1291653832.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ffg11291653832.ps tmp/3ffg11291653832.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ffg11291653832.ps tmp/4ffg11291653832.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ffg11291653832.ps tmp/5ffg11291653832.png",intern=TRUE))
character(0)
> try(system("convert tmp/686y41291653832.ps tmp/686y41291653832.png",intern=TRUE))
character(0)
> try(system("convert tmp/70fx71291653832.ps tmp/70fx71291653832.png",intern=TRUE))
character(0)
> try(system("convert tmp/80fx71291653832.ps tmp/80fx71291653832.png",intern=TRUE))
character(0)
> try(system("convert tmp/90fx71291653832.ps tmp/90fx71291653832.png",intern=TRUE))
character(0)
> try(system("convert tmp/10t7es1291653832.ps tmp/10t7es1291653832.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.730 1.950 6.712