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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'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> x <- array(list(2293.41
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+ ,4.83
+ ,2.7
+ ,2604.42
+ ,2641.65
+ ,2659.81
+ ,2638.53
+ ,2915.02
+ ,12151.11
+ ,10444.5
+ ,-7.7
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+ ,2659.81
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+ ,12974.89
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+ ,2915.02
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+ ,2604.42
+ ,2848.96
+ ,13411.84
+ ,10213.97
+ ,-5.7
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+ ,2915.02
+ ,2892.63
+ ,2833.18
+ ,12708.47
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+ ,2848.96
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+ ,10777.22
+ ,1.9
+ ,8
+ ,5.19
+ ,2.3
+ ,2833.18
+ ,2848.96
+ ,2794.83
+ ,2845.26
+ ,2987.1
+ ,13720.95
+ ,10682.74
+ ,3.6
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+ ,2995.55
+ ,2833.18
+ ,2848.96
+ ,2794.83
+ ,3013.24
+ ,14452.93
+ ,10666.71
+ ,5
+ ,15
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+ ,2.5
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+ ,2995.55
+ ,2833.18
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+ ,3110.52
+ ,14760.87
+ ,10665.78
+ ,4.7
+ ,14
+ ,5.7
+ ,3.1
+ ,3013.24
+ ,2987.1
+ ,2995.55
+ ,2833.18
+ ,3045.78
+ ,15311.7
+ ,10433.56
+ ,5.1
+ ,14
+ ,5.61
+ ,3
+ ,3110.52
+ ,3013.24
+ ,2987.1
+ ,2995.55
+ ,3032.93
+ ,16153.34
+ ,10967.87
+ ,6.6
+ ,10
+ ,5.66
+ ,3.4
+ ,3045.78
+ ,3110.52
+ ,3013.24
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+ ,3045.78
+ ,3110.52
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+ ,3142.95
+ ,3032.93
+ ,3045.78
+ ,3110.52
+ ,2897.06
+ ,16969.28
+ ,10582.92
+ ,8.6
+ ,15
+ ,5.5
+ ,2.7
+ ,3012.61
+ ,3142.95
+ ,3032.93
+ ,3045.78
+ ,2863.36
+ ,17039.97
+ ,10580.27
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+ ,13
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+ ,2.2
+ ,2897.06
+ ,3012.61
+ ,3142.95
+ ,3032.93
+ ,2882.6
+ ,19598.93
+ ,10947.43
+ ,8.6
+ ,12
+ ,5.3
+ ,2.1
+ ,2863.36
+ ,2897.06
+ ,3012.61
+ ,3142.95
+ ,2767.63
+ ,19834.71
+ ,10483.39
+ ,9.2
+ ,13
+ ,5.38
+ ,2.2
+ ,2882.6
+ ,2863.36
+ ,2897.06
+ ,3012.61
+ ,2803.47
+ ,19685.53
+ ,10539.68
+ ,7.7
+ ,11
+ ,5.5
+ ,1.9
+ ,2767.63
+ ,2882.6
+ ,2863.36
+ ,2897.06
+ ,3030.29
+ ,18941.6
+ ,11281.26
+ ,6.4
+ ,9
+ ,5.35
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+ ,2803.47
+ ,2767.63
+ ,2882.6
+ ,2863.36
+ ,3210.52
+ ,18409.96
+ ,11251.2
+ ,8.6
+ ,8
+ ,4.99
+ ,1.9
+ ,3030.29
+ ,2803.47
+ ,2767.63
+ ,2882.6
+ ,3249.57
+ ,18470.97
+ ,10817.9
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+ ,3030.29
+ ,2803.47
+ ,2767.63
+ ,2999.93
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+ ,10394.48
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+ ,5
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+ ,10714.03
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+ ,3
+ ,4.87
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+ ,2999.93
+ ,3249.57
+ ,3210.52
+ ,3030.29
+ ,3053.05
+ ,17671
+ ,10935.47
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+ ,-2
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+ ,0.9
+ ,3181.96
+ ,2999.93
+ ,3249.57
+ ,3210.52
+ ,3092.71
+ ,18033.25
+ ,11052.23
+ ,0
+ ,0
+ ,4.4
+ ,0.7
+ ,3053.05
+ ,3181.96
+ ,2999.93
+ ,3249.57
+ ,3165.26
+ ,17135.96
+ ,10704.02
+ ,-0.9
+ ,-8
+ ,3.99
+ ,0.7
+ ,3092.71
+ ,3053.05
+ ,3181.96
+ ,2999.93
+ ,3173.95
+ ,16505.21
+ ,10853.87
+ ,-0.1
+ ,2
+ ,3.67
+ ,0.8
+ ,3165.26
+ ,3092.71
+ ,3053.05
+ ,3181.96
+ ,3280.37
+ ,16666.97
+ ,10443.5
+ ,-1.4
+ ,2
+ ,3.65
+ ,1.2
+ ,3173.95
+ ,3165.26
+ ,3092.71
+ ,3053.05
+ ,3288.18
+ ,15418.03
+ ,9753.63
+ ,-7.1
+ ,2
+ ,3.75
+ ,1.2
+ ,3280.37
+ ,3173.95
+ ,3165.26
+ ,3092.71
+ ,3411.13
+ ,14153.22
+ ,9327.78
+ ,-6.2
+ ,3
+ ,3.67
+ ,1
+ ,3288.18
+ ,3280.37
+ ,3173.95
+ ,3165.26
+ ,3484.74
+ ,13830.14
+ ,9349.44
+ ,-5.6
+ ,6
+ ,3.68
+ ,1
+ ,3411.13
+ ,3288.18
+ ,3280.37
+ ,3173.95
+ ,3361.13
+ ,14295.79
+ ,9018.68
+ ,-7.6
+ ,1
+ ,3.85
+ ,0.6
+ ,3484.74
+ ,3411.13
+ ,3288.18
+ ,3280.37
+ ,3230.66
+ ,14525.87
+ ,9005.73
+ ,-7.3
+ ,1
+ ,4.02
+ ,0.6
+ ,3361.13
+ ,3484.74
+ ,3411.13
+ ,3288.18
+ ,3006.84
+ ,13486.9
+ ,8164.47
+ ,-7.8
+ ,-4
+ ,3.99
+ ,0.9
+ ,3230.66
+ ,3361.13
+ ,3484.74
+ ,3411.13
+ ,3149.9
+ ,14144.81
+ ,7895.51
+ ,-3.7
+ ,1
+ ,4.12
+ ,0.8
+ ,3006.84
+ ,3230.66
+ ,3361.13
+ ,3484.74
+ ,3403.13
+ ,15243.98
+ ,8478.52
+ ,-0.3
+ ,2
+ ,4.47
+ ,0.4
+ ,3149.9
+ ,3006.84
+ ,3230.66
+ ,3361.13
+ ,3564.95
+ ,16370.17
+ ,9097.14
+ ,2
+ ,3
+ ,4.69
+ ,1
+ ,3403.13
+ ,3149.9
+ ,3006.84
+ ,3230.66
+ ,3327.7
+ ,15231.29
+ ,8872.96
+ ,2
+ ,5
+ ,4.77
+ ,1.6
+ ,3564.95
+ ,3403.13
+ ,3149.9
+ ,3006.84
+ ,3141.12
+ ,15514.27
+ ,9081.69
+ ,3.1
+ ,5
+ ,4.92
+ ,1.9
+ ,3327.7
+ ,3564.95
+ ,3403.13
+ ,3149.9
+ ,3064.42
+ ,15941.29
+ ,9037.44
+ ,2.7
+ ,3
+ ,4.84
+ ,1.5
+ ,3141.12
+ ,3327.7
+ ,3564.95
+ ,3403.13
+ ,2880.4
+ ,16840.31
+ ,8709.47
+ ,2.4
+ ,2
+ ,4.77
+ ,1
+ ,3064.42
+ ,3141.12
+ ,3327.7
+ ,3564.95
+ ,2661.39
+ ,16797.69
+ ,8323.61
+ ,2
+ ,3
+ ,4.88
+ ,0.7
+ ,2880.4
+ ,3064.42
+ ,3141.12
+ ,3327.7
+ ,2504.67
+ ,15929.69
+ ,7808.33
+ ,4.1
+ ,-1
+ ,5
+ ,0.4
+ ,2661.39
+ ,2880.4
+ ,3064.42
+ ,3141.12
+ ,2450.41
+ ,15917.07
+ ,7909.82
+ ,5.2
+ ,-9
+ ,5.3
+ ,1.1
+ ,2504.67
+ ,2661.39
+ ,2880.4
+ ,3064.42
+ ,2354.32
+ ,16135.96
+ ,7683.23
+ ,6
+ ,-5
+ ,5.5
+ ,1.4
+ ,2450.41
+ ,2504.67
+ ,2661.39
+ ,2880.4
+ ,2401.33
+ ,17274.75
+ ,7875.82
+ ,5.1
+ ,-1
+ ,5.44
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+ ,2354.32
+ ,2450.41
+ ,2504.67
+ ,2661.39
+ ,2394.36
+ ,18233.45
+ ,7855.04
+ ,3.6
+ ,-9
+ ,5.36
+ ,1.6
+ ,2401.33
+ ,2354.32
+ ,2450.41
+ ,2504.67
+ ,2409.36
+ ,19081.79
+ ,7948.43
+ ,0.8
+ ,-8
+ ,5.32
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+ ,2394.36
+ ,2401.33
+ ,2354.32
+ ,2450.41
+ ,2525.56
+ ,20152.53
+ ,7990.65
+ ,1.8
+ ,-12
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+ ,2409.36
+ ,2394.36
+ ,2401.33
+ ,2354.32
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+ ,20482.48
+ ,7603.88
+ ,0.1
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+ ,2525.56
+ ,2409.36
+ ,2394.36
+ ,2401.33
+ ,2250.27
+ ,20021.79
+ ,7242.41
+ ,-2
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+ ,2346.9
+ ,2525.56
+ ,2409.36
+ ,2394.36
+ ,2152.18
+ ,18200.34
+ ,6657.9
+ ,-4.1
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+ ,2250.27
+ ,2346.9
+ ,2525.56
+ ,2409.36
+ ,2154.87
+ ,18255.96
+ ,6901.12
+ ,-3
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+ ,2346.9
+ ,2525.56
+ ,2097.76
+ ,18555.87
+ ,6921.03
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+ ,2154.87
+ ,2152.18
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+ ,1989.31
+ ,18223.28
+ ,6707.03
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+ ,2097.76
+ ,2154.87
+ ,2152.18
+ ,2250.27
+ ,1877.1
+ ,20090.77
+ ,6435.87
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+ ,-8
+ ,5.05
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+ ,1989.31
+ ,2097.76
+ ,2154.87
+ ,2152.18
+ ,1852.13
+ ,21021.37
+ ,6323.43
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+ ,-8
+ ,5.23
+ ,2.4
+ ,1877.1
+ ,1989.31
+ ,2097.76
+ ,2154.87
+ ,1795.65
+ ,21108.13
+ ,5996.21
+ ,-6.2
+ ,-9
+ ,5.3
+ ,2.5
+ ,1852.13
+ ,1877.1
+ ,1989.31
+ ,2097.76
+ ,1751.01
+ ,20824.57
+ ,5804.8
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+ ,5.69
+ ,2
+ ,1795.65
+ ,1852.13
+ ,1877.1
+ ,1989.31
+ ,1745.74
+ ,20870.31
+ ,5685.5
+ ,-7.8
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+ ,1751.01
+ ,1795.65
+ ,1852.13
+ ,1877.1
+ ,1703.45
+ ,21597.85
+ ,5496.26
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+ ,1745.74
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+ ,1852.13
+ ,1748.09
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+ ,1703.45
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+ ,21761.31
+ ,5600.81
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+ ,1751.01
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+ ,21837.95
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+ ,1690.6
+ ,20394.71
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+ ,1748.09
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+ ,1665.5
+ ,20675.4
+ ,5518.24
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+ ,-18
+ ,6.26
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+ ,1690.6
+ ,1711.74
+ ,1734.1
+ ,1748.09
+ ,1631.59
+ ,20407.27
+ ,5174.59
+ ,-14
+ ,-16
+ ,6.07
+ ,2
+ ,1665.5
+ ,1690.6
+ ,1711.74
+ ,1734.1
+ ,1538.09
+ ,19417.95
+ ,5136.72
+ ,-12.4
+ ,-16
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+ ,1711.74
+ ,1452.46
+ ,18111.66
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+ ,1690.6
+ ,1429.12
+ ,17961.87
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+ ,-9.9
+ ,-16
+ ,6.99
+ ,1.2
+ ,1452.46
+ ,1538.09
+ ,1631.59
+ ,1665.5
+ ,1471.16
+ ,18128.65
+ ,4744.66
+ ,-9.9
+ ,-12
+ ,6.94
+ ,1.2
+ ,1429.12
+ ,1452.46
+ ,1538.09
+ ,1631.59
+ ,1475.57
+ ,17410.71
+ ,4637.58
+ ,-9.4
+ ,-12
+ ,7.14
+ ,1.3
+ ,1471.16
+ ,1429.12
+ ,1452.46
+ ,1538.09
+ ,1464.65
+ ,16188.69
+ ,4684.76
+ ,-9.2
+ ,-6
+ ,7.35
+ ,1.2
+ ,1475.57
+ ,1471.16
+ ,1429.12
+ ,1452.46
+ ,1433.75
+ ,15039.44
+ ,4510.76
+ ,-7
+ ,-5
+ ,7.48
+ ,1.3
+ ,1464.65
+ ,1475.57
+ ,1471.16
+ ,1429.12
+ ,1451.04
+ ,16373.21
+ ,4392.16
+ ,-5.1
+ ,-7
+ ,7.74
+ ,1.4
+ ,1433.75
+ ,1464.65
+ ,1475.57
+ ,1471.16
+ ,1365.41
+ ,16322.08
+ ,4230.64
+ ,-2.2
+ ,-4
+ ,8.1
+ ,1.7
+ ,1451.04
+ ,1433.75
+ ,1464.65
+ ,1475.57
+ ,1299.88
+ ,16433.75
+ ,4064.33
+ ,0.4
+ ,-7
+ ,8.29
+ ,1.7
+ ,1365.41
+ ,1451.04
+ ,1433.75
+ ,1464.65
+ ,1349.03
+ ,18065.03
+ ,3953.66
+ ,4.3
+ ,-8
+ ,8.26
+ ,1.8
+ ,1299.88
+ ,1365.41
+ ,1451.04
+ ,1433.75
+ ,1368.43
+ ,19036.23
+ ,3872.33
+ ,3.7
+ ,-7
+ ,8.41
+ ,1.9
+ ,1349.03
+ ,1299.88
+ ,1365.41
+ ,1451.04)
+ ,dim=c(11
+ ,176)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Rend_oblig_EUR'
+ ,'Alg_consumptie_index_BE'
+ ,'Y1'
+ ,'Y2'
+ ,'Y3'
+ ,'Y4')
+ ,1:176))
> y <- array(NA,dim=c(11,176),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Conjunct_Seizoenzuiver','Cons_vertrouw','Rend_oblig_EUR','Alg_consumptie_index_BE','Y1','Y2','Y3','Y4'),1:176))
> 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
BEL_20 Nikkei DJ_Indust Conjunct_Seizoenzuiver Cons_vertrouw
1 2293.41 10430.35 9374.63 -18.2 -11
2 2070.83 9691.12 8679.75 -22.8 -17
3 2029.60 9810.31 8593.00 -23.6 -18
4 2052.02 9304.43 8398.37 -27.6 -19
5 1864.44 8767.96 7992.12 -29.4 -22
6 1670.07 7764.58 7235.47 -31.8 -24
7 1810.99 7694.78 7690.50 -31.4 -24
8 1905.41 8331.49 8396.20 -27.6 -20
9 1862.83 8460.94 8595.56 -28.8 -25
10 2014.45 8531.45 8614.55 -21.9 -22
11 2197.82 9117.03 9181.73 -13.9 -17
12 2962.34 12123.53 11114.08 -8.0 -9
13 3047.03 12989.35 11530.75 -2.8 -11
14 3032.60 13168.91 11322.38 -3.3 -13
15 3504.37 14084.60 12056.67 -1.3 -11
16 3801.06 13995.33 12812.48 0.5 -9
17 3857.62 13357.70 12656.63 -1.9 -7
18 3674.40 12602.93 12193.88 2.0 -3
19 3720.98 13547.84 12419.57 1.7 -3
20 3844.49 13731.31 12538.12 1.9 -6
21 4116.68 15532.18 13406.97 0.1 -4
22 4105.18 15543.76 13200.58 2.4 -8
23 4435.23 16903.36 13901.28 2.3 -1
24 4296.49 16235.39 13557.69 4.7 -2
25 4202.52 16460.95 13239.71 5.0 -2
26 4562.84 17974.77 13673.28 7.2 -1
27 4621.40 18001.37 13480.21 8.5 1
28 4696.96 17611.14 13407.75 6.8 2
29 4591.27 17460.53 12754.80 5.8 2
30 4356.98 17128.37 12268.53 3.7 -1
31 4502.64 17741.23 12631.48 4.8 1
32 4443.91 17286.32 12512.89 6.1 -1
33 4290.89 16775.08 12377.62 6.9 -8
34 4199.75 16101.07 12185.15 5.7 1
35 4138.52 16519.44 11963.12 6.9 2
36 3970.10 15934.09 11533.59 5.5 -2
37 3862.27 15786.78 11257.35 6.5 -2
38 3701.61 15147.55 11036.89 7.7 -2
39 3570.12 14990.31 10997.97 6.3 -2
40 3801.06 16397.83 11333.88 5.5 -6
41 3895.51 17232.97 11234.68 5.3 -4
42 3917.96 16311.54 11145.65 3.3 -5
43 3813.06 16187.64 10971.19 2.2 -2
44 3667.03 16102.64 10872.48 0.6 -1
45 3494.17 15650.83 10827.81 0.2 -5
46 3363.99 14368.05 10695.25 -0.7 -9
47 3295.32 13392.79 10324.31 -1.7 -8
48 3277.01 12986.62 10532.54 -3.7 -14
49 3257.16 12204.98 10554.27 -7.6 -10
50 3161.69 11716.87 10545.38 -8.2 -11
51 3097.31 11402.75 10486.64 -7.5 -11
52 3061.26 11082.38 10377.18 -8.0 -11
53 3119.31 11395.64 10283.19 -6.9 -5
54 3106.22 11809.38 10682.06 -4.2 -2
55 3080.58 11545.71 10723.78 -3.6 -3
56 2981.85 11394.84 10539.51 -1.8 -6
57 2921.44 11068.05 10673.38 -3.2 -6
58 2849.27 10973.00 10411.75 -1.3 -7
59 2756.76 11028.93 10001.60 0.6 -6
60 2645.64 11079.42 10204.59 1.2 -2
61 2497.84 10989.34 10032.80 0.4 -2
62 2448.05 11383.89 10152.09 3.0 -4
63 2454.62 11527.72 10364.91 -0.4 0
64 2407.60 11037.54 10092.96 0.0 -6
65 2472.81 11950.95 10418.40 -1.3 -4
66 2408.64 11441.08 10323.73 -3.1 -3
67 2440.25 10631.92 10601.61 -4.0 -1
68 2350.44 10892.76 10540.05 -4.9 -3
69 2196.72 10295.98 10124.63 -4.6 -6
70 2174.56 10205.29 9762.12 -5.4 -6
71 2120.88 10717.13 9682.35 -8.1 -15
72 2093.48 10637.44 9492.49 -9.4 -5
73 2061.41 9884.59 9284.73 -12.6 -11
74 1969.60 9676.31 9154.34 -15.7 -13
75 1959.67 8895.71 9098.03 -17.3 -10
76 1910.43 8145.82 8623.36 -14.4 -9
77 1833.42 7905.84 8334.59 -16.2 -11
78 1635.25 8169.75 7977.64 -14.9 -18
79 1765.90 8538.47 7916.13 -11.0 -13
80 1946.81 8570.73 8474.21 -11.5 -9
81 1995.37 8692.94 8526.63 -9.6 -8
82 2042.00 8721.14 8641.21 -8.8 -4
83 1940.49 8792.50 8048.10 -9.7 -3
84 2065.81 9354.01 8160.67 -8.4 -3
85 2214.95 9751.20 8685.40 -8.4 -3
86 2304.98 10352.27 8616.49 -6.8 -1
87 2555.28 10965.88 9492.44 -5.3 0
88 2799.43 11717.46 10080.48 -5.1 1
89 2811.70 11384.49 10179.35 -6.5 0
90 2735.70 11448.79 10500.98 -7.3 2
91 2745.88 9981.65 9892.56 -10.8 1
92 2720.25 10300.79 9923.81 -10.9 -1
93 2638.53 10496.20 9978.53 -13.4 -8
94 2659.81 10511.22 9721.84 -15.5 -18
95 2641.65 10438.90 9220.75 -15.4 -14
96 2604.42 9996.83 9042.56 -11.9 -4
97 2892.63 11576.21 10314.68 -8.0 0
98 2915.02 12151.11 10444.50 -7.7 4
99 2845.26 12974.89 10767.20 -6.4 4
100 2794.83 13975.55 10546.82 -5.6 3
101 2848.96 13411.84 10213.97 -5.7 3
102 2833.18 12708.47 10052.60 -0.1 7
103 2995.55 13266.27 10777.22 1.9 8
104 2987.10 13720.95 10682.74 3.6 13
105 3013.24 14452.93 10666.71 5.0 15
106 3110.52 14760.87 10665.78 4.7 14
107 3045.78 15311.70 10433.56 5.1 14
108 3032.93 16153.34 10967.87 6.6 10
109 3142.95 16329.89 11014.51 6.0 16
110 3012.61 16973.38 10654.41 6.2 13
111 2897.06 16969.28 10582.92 8.6 15
112 2863.36 17039.97 10580.27 7.4 13
113 2882.60 19598.93 10947.43 8.6 12
114 2767.63 19834.71 10483.39 9.2 13
115 2803.47 19685.53 10539.68 7.7 11
116 3030.29 18941.60 11281.26 6.4 9
117 3210.52 18409.96 11251.20 8.6 8
118 3249.57 18470.97 10817.90 6.4 8
119 2999.93 17677.90 10394.48 6.0 5
120 3181.96 17544.22 10714.03 2.6 3
121 3053.05 17671.00 10935.47 0.1 -2
122 3092.71 18033.25 11052.23 0.0 0
123 3165.26 17135.96 10704.02 -0.9 -8
124 3173.95 16505.21 10853.87 -0.1 2
125 3280.37 16666.97 10443.50 -1.4 2
126 3288.18 15418.03 9753.63 -7.1 2
127 3411.13 14153.22 9327.78 -6.2 3
128 3484.74 13830.14 9349.44 -5.6 6
129 3361.13 14295.79 9018.68 -7.6 1
130 3230.66 14525.87 9005.73 -7.3 1
131 3006.84 13486.90 8164.47 -7.8 -4
132 3149.90 14144.81 7895.51 -3.7 1
133 3403.13 15243.98 8478.52 -0.3 2
134 3564.95 16370.17 9097.14 2.0 3
135 3327.70 15231.29 8872.96 2.0 5
136 3141.12 15514.27 9081.69 3.1 5
137 3064.42 15941.29 9037.44 2.7 3
138 2880.40 16840.31 8709.47 2.4 2
139 2661.39 16797.69 8323.61 2.0 3
140 2504.67 15929.69 7808.33 4.1 -1
141 2450.41 15917.07 7909.82 5.2 -9
142 2354.32 16135.96 7683.23 6.0 -5
143 2401.33 17274.75 7875.82 5.1 -1
144 2394.36 18233.45 7855.04 3.6 -9
145 2409.36 19081.79 7948.43 0.8 -8
146 2525.56 20152.53 7990.65 1.8 -12
147 2346.90 20482.48 7603.88 0.1 -13
148 2250.27 20021.79 7242.41 -2.0 -16
149 2152.18 18200.34 6657.90 -4.1 -21
150 2154.87 18255.96 6901.12 -3.0 -21
151 2097.76 18555.87 6921.03 -3.1 -16
152 1989.31 18223.28 6707.03 -3.9 -15
153 1877.10 20090.77 6435.87 -4.8 -8
154 1852.13 21021.37 6323.43 -5.1 -8
155 1795.65 21108.13 5996.21 -6.2 -9
156 1751.01 20824.57 5804.80 -6.6 -9
157 1745.74 20870.31 5685.50 -7.8 -11
158 1703.45 21597.85 5496.26 -10.5 -12
159 1748.09 22204.88 5671.51 -10.8 -13
160 1734.10 21761.31 5600.81 -12.3 -13
161 1711.74 21837.95 5580.18 -13.0 -12
162 1690.60 20394.71 5610.95 -12.8 -15
163 1665.50 20675.40 5518.24 -15.1 -18
164 1631.59 20407.27 5174.59 -14.0 -16
165 1538.09 19417.95 5136.72 -12.4 -16
166 1452.46 18111.66 4935.80 -11.8 -20
167 1429.12 17961.87 4761.28 -9.9 -16
168 1471.16 18128.65 4744.66 -9.9 -12
169 1475.57 17410.71 4637.58 -9.4 -12
170 1464.65 16188.69 4684.76 -9.2 -6
171 1433.75 15039.44 4510.76 -7.0 -5
172 1451.04 16373.21 4392.16 -5.1 -7
173 1365.41 16322.08 4230.64 -2.2 -4
174 1299.88 16433.75 4064.33 0.4 -7
175 1349.03 18065.03 3953.66 4.3 -8
176 1368.43 19036.23 3872.33 3.7 -7
Rend_oblig_EUR Alg_consumptie_index_BE Y1 Y2 Y3 Y4 M1
1 3.30 -0.8 2443.27 2513.17 2466.92 2502.66 1
2 3.47 -1.7 2293.41 2443.27 2513.17 2466.92 0
3 3.72 -1.1 2070.83 2293.41 2443.27 2513.17 0
4 3.67 -0.4 2029.60 2070.83 2293.41 2443.27 0
5 3.82 0.6 2052.02 2029.60 2070.83 2293.41 0
6 3.85 0.6 1864.44 2052.02 2029.60 2070.83 0
7 3.90 1.9 1670.07 1864.44 2052.02 2029.60 0
8 3.99 2.3 1810.99 1670.07 1864.44 2052.02 0
9 4.35 2.6 1905.41 1810.99 1670.07 1864.44 0
10 4.98 3.1 1862.83 1905.41 1810.99 1670.07 0
11 5.46 4.7 2014.45 1862.83 1905.41 1810.99 0
12 5.19 5.5 2197.82 2014.45 1862.83 1905.41 0
13 5.03 5.4 2962.34 2197.82 2014.45 1862.83 1
14 5.38 5.9 3047.03 2962.34 2197.82 2014.45 0
15 5.37 5.8 3032.60 3047.03 2962.34 2197.82 0
16 4.87 5.2 3504.37 3032.60 3047.03 2962.34 0
17 4.70 4.2 3801.06 3504.37 3032.60 3047.03 0
18 4.40 4.4 3857.62 3801.06 3504.37 3032.60 0
19 4.37 3.6 3674.40 3857.62 3801.06 3504.37 0
20 4.54 3.5 3720.98 3674.40 3857.62 3801.06 0
21 4.80 3.1 3844.49 3720.98 3674.40 3857.62 0
22 4.56 2.9 4116.68 3844.49 3720.98 3674.40 0
23 4.61 2.2 4105.18 4116.68 3844.49 3720.98 0
24 4.58 1.5 4435.23 4105.18 4116.68 3844.49 0
25 4.61 1.1 4296.49 4435.23 4105.18 4116.68 1
26 4.77 1.4 4202.52 4296.49 4435.23 4105.18 0
27 4.76 1.3 4562.84 4202.52 4296.49 4435.23 0
28 4.50 1.3 4621.40 4562.84 4202.52 4296.49 0
29 4.37 1.8 4696.96 4621.40 4562.84 4202.52 0
30 4.15 1.8 4591.27 4696.96 4621.40 4562.84 0
31 4.24 1.8 4356.98 4591.27 4696.96 4621.40 0
32 4.22 1.7 4502.64 4356.98 4591.27 4696.96 0
33 4.01 1.6 4443.91 4502.64 4356.98 4591.27 0
34 3.93 1.5 4290.89 4443.91 4502.64 4356.98 0
35 3.97 1.2 4199.75 4290.89 4443.91 4502.64 0
36 3.92 1.2 4138.52 4199.75 4290.89 4443.91 0
37 3.99 1.6 3970.10 4138.52 4199.75 4290.89 1
38 4.10 1.6 3862.27 3970.10 4138.52 4199.75 0
39 4.04 1.9 3701.61 3862.27 3970.10 4138.52 0
40 3.97 2.2 3570.12 3701.61 3862.27 3970.10 0
41 3.90 2.0 3801.06 3570.12 3701.61 3862.27 0
42 3.66 1.7 3895.51 3801.06 3570.12 3701.61 0
43 3.44 2.4 3917.96 3895.51 3801.06 3570.12 0
44 3.27 2.6 3813.06 3917.96 3895.51 3801.06 0
45 3.24 2.9 3667.03 3813.06 3917.96 3895.51 0
46 3.27 2.6 3494.17 3667.03 3813.06 3917.96 0
47 2.99 2.5 3363.99 3494.17 3667.03 3813.06 0
48 2.77 3.2 3295.32 3363.99 3494.17 3667.03 0
49 2.90 3.1 3277.01 3295.32 3363.99 3494.17 1
50 2.87 3.1 3257.16 3277.01 3295.32 3363.99 0
51 2.84 2.9 3161.69 3257.16 3277.01 3295.32 0
52 3.02 2.5 3097.31 3161.69 3257.16 3277.01 0
53 3.19 2.8 3061.26 3097.31 3161.69 3257.16 0
54 3.39 3.1 3119.31 3061.26 3097.31 3161.69 0
55 3.28 2.6 3106.22 3119.31 3061.26 3097.31 0
56 3.28 2.3 3080.58 3106.22 3119.31 3061.26 0
57 3.33 2.3 2981.85 3080.58 3106.22 3119.31 0
58 3.51 2.6 2921.44 2981.85 3080.58 3106.22 0
59 3.65 2.9 2849.27 2921.44 2981.85 3080.58 0
60 3.76 2.0 2756.76 2849.27 2921.44 2981.85 0
61 3.67 2.2 2645.64 2756.76 2849.27 2921.44 1
62 3.87 2.4 2497.84 2645.64 2756.76 2849.27 0
63 3.99 2.3 2448.05 2497.84 2645.64 2756.76 0
64 3.90 2.6 2454.62 2448.05 2497.84 2645.64 0
65 3.74 1.9 2407.60 2454.62 2448.05 2497.84 0
66 3.55 1.1 2472.81 2407.60 2454.62 2448.05 0
67 3.67 1.3 2408.64 2472.81 2407.60 2454.62 0
68 3.60 1.6 2440.25 2408.64 2472.81 2407.60 0
69 3.82 1.7 2350.44 2440.25 2408.64 2472.81 0
70 3.91 1.9 2196.72 2350.44 2440.25 2408.64 0
71 3.79 1.6 2174.56 2196.72 2350.44 2440.25 0
72 3.73 1.8 2120.88 2174.56 2196.72 2350.44 0
73 3.77 1.8 2093.48 2120.88 2174.56 2196.72 1
74 3.47 1.5 2061.41 2093.48 2120.88 2174.56 0
75 3.18 1.6 1969.60 2061.41 2093.48 2120.88 0
76 3.44 1.0 1959.67 1969.60 2061.41 2093.48 0
77 3.81 1.5 1910.43 1959.67 1969.60 2061.41 0
78 3.60 1.8 1833.42 1910.43 1959.67 1969.60 0
79 3.42 1.7 1635.25 1833.42 1910.43 1959.67 0
80 3.73 1.2 1765.90 1635.25 1833.42 1910.43 0
81 4.04 1.4 1946.81 1765.90 1635.25 1833.42 0
82 4.22 1.1 1995.37 1946.81 1765.90 1635.25 0
83 4.30 1.3 2042.00 1995.37 1946.81 1765.90 0
84 4.28 1.3 1940.49 2042.00 1995.37 1946.81 0
85 4.56 1.3 2065.81 1940.49 2042.00 1995.37 1
86 4.79 1.3 2214.95 2065.81 1940.49 2042.00 0
87 4.93 0.9 2304.98 2214.95 2065.81 1940.49 0
88 5.12 1.3 2555.28 2304.98 2214.95 2065.81 0
89 5.13 1.8 2799.43 2555.28 2304.98 2214.95 0
90 5.15 2.7 2811.70 2799.43 2555.28 2304.98 0
91 4.92 2.6 2735.70 2811.70 2799.43 2555.28 0
92 4.79 2.9 2745.88 2735.70 2811.70 2799.43 0
93 4.68 2.2 2720.25 2745.88 2735.70 2811.70 0
94 4.42 2.1 2638.53 2720.25 2745.88 2735.70 0
95 4.53 2.3 2659.81 2638.53 2720.25 2745.88 0
96 4.71 2.3 2641.65 2659.81 2638.53 2720.25 0
97 4.83 2.7 2604.42 2641.65 2659.81 2638.53 1
98 5.04 2.6 2892.63 2604.42 2641.65 2659.81 0
99 5.06 2.9 2915.02 2892.63 2604.42 2641.65 0
100 5.14 3.1 2845.26 2915.02 2892.63 2604.42 0
101 5.06 2.8 2794.83 2845.26 2915.02 2892.63 0
102 5.04 2.1 2848.96 2794.83 2845.26 2915.02 0
103 5.19 2.3 2833.18 2848.96 2794.83 2845.26 0
104 5.22 2.2 2995.55 2833.18 2848.96 2794.83 0
105 5.40 2.5 2987.10 2995.55 2833.18 2848.96 0
106 5.70 3.1 3013.24 2987.10 2995.55 2833.18 0
107 5.61 3.0 3110.52 3013.24 2987.10 2995.55 0
108 5.66 3.4 3045.78 3110.52 3013.24 2987.10 0
109 5.65 2.9 3032.93 3045.78 3110.52 3013.24 1
110 5.63 2.8 3142.95 3032.93 3045.78 3110.52 0
111 5.50 2.7 3012.61 3142.95 3032.93 3045.78 0
112 5.61 2.2 2897.06 3012.61 3142.95 3032.93 0
113 5.30 2.1 2863.36 2897.06 3012.61 3142.95 0
114 5.38 2.2 2882.60 2863.36 2897.06 3012.61 0
115 5.50 1.9 2767.63 2882.60 2863.36 2897.06 0
116 5.35 1.8 2803.47 2767.63 2882.60 2863.36 0
117 4.99 1.9 3030.29 2803.47 2767.63 2882.60 0
118 4.93 1.5 3210.52 3030.29 2803.47 2767.63 0
119 5.16 1.3 3249.57 3210.52 3030.29 2803.47 0
120 4.87 1.2 2999.93 3249.57 3210.52 3030.29 0
121 4.73 0.9 3181.96 2999.93 3249.57 3210.52 1
122 4.40 0.7 3053.05 3181.96 2999.93 3249.57 0
123 3.99 0.7 3092.71 3053.05 3181.96 2999.93 0
124 3.67 0.8 3165.26 3092.71 3053.05 3181.96 0
125 3.65 1.2 3173.95 3165.26 3092.71 3053.05 0
126 3.75 1.2 3280.37 3173.95 3165.26 3092.71 0
127 3.67 1.0 3288.18 3280.37 3173.95 3165.26 0
128 3.68 1.0 3411.13 3288.18 3280.37 3173.95 0
129 3.85 0.6 3484.74 3411.13 3288.18 3280.37 0
130 4.02 0.6 3361.13 3484.74 3411.13 3288.18 0
131 3.99 0.9 3230.66 3361.13 3484.74 3411.13 0
132 4.12 0.8 3006.84 3230.66 3361.13 3484.74 0
133 4.47 0.4 3149.90 3006.84 3230.66 3361.13 1
134 4.69 1.0 3403.13 3149.90 3006.84 3230.66 0
135 4.77 1.6 3564.95 3403.13 3149.90 3006.84 0
136 4.92 1.9 3327.70 3564.95 3403.13 3149.90 0
137 4.84 1.5 3141.12 3327.70 3564.95 3403.13 0
138 4.77 1.0 3064.42 3141.12 3327.70 3564.95 0
139 4.88 0.7 2880.40 3064.42 3141.12 3327.70 0
140 5.00 0.4 2661.39 2880.40 3064.42 3141.12 0
141 5.30 1.1 2504.67 2661.39 2880.40 3064.42 0
142 5.50 1.4 2450.41 2504.67 2661.39 2880.40 0
143 5.44 1.3 2354.32 2450.41 2504.67 2661.39 0
144 5.36 1.6 2401.33 2354.32 2450.41 2504.67 0
145 5.32 1.8 2394.36 2401.33 2354.32 2450.41 1
146 5.26 1.9 2409.36 2394.36 2401.33 2354.32 0
147 5.42 1.7 2525.56 2409.36 2394.36 2401.33 0
148 5.37 1.6 2346.90 2525.56 2409.36 2394.36 0
149 5.32 1.3 2250.27 2346.90 2525.56 2409.36 0
150 5.11 1.5 2152.18 2250.27 2346.90 2525.56 0
151 4.82 2.0 2154.87 2152.18 2250.27 2346.90 0
152 4.89 2.3 2097.76 2154.87 2152.18 2250.27 0
153 5.05 2.5 1989.31 2097.76 2154.87 2152.18 0
154 5.23 2.4 1877.10 1989.31 2097.76 2154.87 0
155 5.30 2.5 1852.13 1877.10 1989.31 2097.76 0
156 5.69 2.0 1795.65 1852.13 1877.10 1989.31 0
157 5.86 1.9 1751.01 1795.65 1852.13 1877.10 1
158 5.96 1.9 1745.74 1751.01 1795.65 1852.13 0
159 6.09 1.8 1703.45 1745.74 1751.01 1795.65 0
160 6.11 1.9 1748.09 1703.45 1745.74 1751.01 0
161 6.37 2.0 1734.10 1748.09 1703.45 1745.74 0
162 6.45 2.0 1711.74 1734.10 1748.09 1703.45 0
163 6.26 1.9 1690.60 1711.74 1734.10 1748.09 0
164 6.07 2.0 1665.50 1690.60 1711.74 1734.10 0
165 6.40 1.5 1631.59 1665.50 1690.60 1711.74 0
166 6.72 1.5 1538.09 1631.59 1665.50 1690.60 0
167 6.99 1.2 1452.46 1538.09 1631.59 1665.50 0
168 6.94 1.2 1429.12 1452.46 1538.09 1631.59 0
169 7.14 1.3 1471.16 1429.12 1452.46 1538.09 1
170 7.35 1.2 1475.57 1471.16 1429.12 1452.46 0
171 7.48 1.3 1464.65 1475.57 1471.16 1429.12 0
172 7.74 1.4 1433.75 1464.65 1475.57 1471.16 0
173 8.10 1.7 1451.04 1433.75 1464.65 1475.57 0
174 8.29 1.7 1365.41 1451.04 1433.75 1464.65 0
175 8.26 1.8 1299.88 1365.41 1451.04 1433.75 0
176 8.41 1.9 1349.03 1299.88 1365.41 1451.04 0
M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 t
1 0 0 0 0 0 0 0 0 0 0 1
2 1 0 0 0 0 0 0 0 0 0 2
3 0 1 0 0 0 0 0 0 0 0 3
4 0 0 1 0 0 0 0 0 0 0 4
5 0 0 0 1 0 0 0 0 0 0 5
6 0 0 0 0 1 0 0 0 0 0 6
7 0 0 0 0 0 1 0 0 0 0 7
8 0 0 0 0 0 0 1 0 0 0 8
9 0 0 0 0 0 0 0 1 0 0 9
10 0 0 0 0 0 0 0 0 1 0 10
11 0 0 0 0 0 0 0 0 0 1 11
12 0 0 0 0 0 0 0 0 0 0 12
13 0 0 0 0 0 0 0 0 0 0 13
14 1 0 0 0 0 0 0 0 0 0 14
15 0 1 0 0 0 0 0 0 0 0 15
16 0 0 1 0 0 0 0 0 0 0 16
17 0 0 0 1 0 0 0 0 0 0 17
18 0 0 0 0 1 0 0 0 0 0 18
19 0 0 0 0 0 1 0 0 0 0 19
20 0 0 0 0 0 0 1 0 0 0 20
21 0 0 0 0 0 0 0 1 0 0 21
22 0 0 0 0 0 0 0 0 1 0 22
23 0 0 0 0 0 0 0 0 0 1 23
24 0 0 0 0 0 0 0 0 0 0 24
25 0 0 0 0 0 0 0 0 0 0 25
26 1 0 0 0 0 0 0 0 0 0 26
27 0 1 0 0 0 0 0 0 0 0 27
28 0 0 1 0 0 0 0 0 0 0 28
29 0 0 0 1 0 0 0 0 0 0 29
30 0 0 0 0 1 0 0 0 0 0 30
31 0 0 0 0 0 1 0 0 0 0 31
32 0 0 0 0 0 0 1 0 0 0 32
33 0 0 0 0 0 0 0 1 0 0 33
34 0 0 0 0 0 0 0 0 1 0 34
35 0 0 0 0 0 0 0 0 0 1 35
36 0 0 0 0 0 0 0 0 0 0 36
37 0 0 0 0 0 0 0 0 0 0 37
38 1 0 0 0 0 0 0 0 0 0 38
39 0 1 0 0 0 0 0 0 0 0 39
40 0 0 1 0 0 0 0 0 0 0 40
41 0 0 0 1 0 0 0 0 0 0 41
42 0 0 0 0 1 0 0 0 0 0 42
43 0 0 0 0 0 1 0 0 0 0 43
44 0 0 0 0 0 0 1 0 0 0 44
45 0 0 0 0 0 0 0 1 0 0 45
46 0 0 0 0 0 0 0 0 1 0 46
47 0 0 0 0 0 0 0 0 0 1 47
48 0 0 0 0 0 0 0 0 0 0 48
49 0 0 0 0 0 0 0 0 0 0 49
50 1 0 0 0 0 0 0 0 0 0 50
51 0 1 0 0 0 0 0 0 0 0 51
52 0 0 1 0 0 0 0 0 0 0 52
53 0 0 0 1 0 0 0 0 0 0 53
54 0 0 0 0 1 0 0 0 0 0 54
55 0 0 0 0 0 1 0 0 0 0 55
56 0 0 0 0 0 0 1 0 0 0 56
57 0 0 0 0 0 0 0 1 0 0 57
58 0 0 0 0 0 0 0 0 1 0 58
59 0 0 0 0 0 0 0 0 0 1 59
60 0 0 0 0 0 0 0 0 0 0 60
61 0 0 0 0 0 0 0 0 0 0 61
62 1 0 0 0 0 0 0 0 0 0 62
63 0 1 0 0 0 0 0 0 0 0 63
64 0 0 1 0 0 0 0 0 0 0 64
65 0 0 0 1 0 0 0 0 0 0 65
66 0 0 0 0 1 0 0 0 0 0 66
67 0 0 0 0 0 1 0 0 0 0 67
68 0 0 0 0 0 0 1 0 0 0 68
69 0 0 0 0 0 0 0 1 0 0 69
70 0 0 0 0 0 0 0 0 1 0 70
71 0 0 0 0 0 0 0 0 0 1 71
72 0 0 0 0 0 0 0 0 0 0 72
73 0 0 0 0 0 0 0 0 0 0 73
74 1 0 0 0 0 0 0 0 0 0 74
75 0 1 0 0 0 0 0 0 0 0 75
76 0 0 1 0 0 0 0 0 0 0 76
77 0 0 0 1 0 0 0 0 0 0 77
78 0 0 0 0 1 0 0 0 0 0 78
79 0 0 0 0 0 1 0 0 0 0 79
80 0 0 0 0 0 0 1 0 0 0 80
81 0 0 0 0 0 0 0 1 0 0 81
82 0 0 0 0 0 0 0 0 1 0 82
83 0 0 0 0 0 0 0 0 0 1 83
84 0 0 0 0 0 0 0 0 0 0 84
85 0 0 0 0 0 0 0 0 0 0 85
86 1 0 0 0 0 0 0 0 0 0 86
87 0 1 0 0 0 0 0 0 0 0 87
88 0 0 1 0 0 0 0 0 0 0 88
89 0 0 0 1 0 0 0 0 0 0 89
90 0 0 0 0 1 0 0 0 0 0 90
91 0 0 0 0 0 1 0 0 0 0 91
92 0 0 0 0 0 0 1 0 0 0 92
93 0 0 0 0 0 0 0 1 0 0 93
94 0 0 0 0 0 0 0 0 1 0 94
95 0 0 0 0 0 0 0 0 0 1 95
96 0 0 0 0 0 0 0 0 0 0 96
97 0 0 0 0 0 0 0 0 0 0 97
98 1 0 0 0 0 0 0 0 0 0 98
99 0 1 0 0 0 0 0 0 0 0 99
100 0 0 1 0 0 0 0 0 0 0 100
101 0 0 0 1 0 0 0 0 0 0 101
102 0 0 0 0 1 0 0 0 0 0 102
103 0 0 0 0 0 1 0 0 0 0 103
104 0 0 0 0 0 0 1 0 0 0 104
105 0 0 0 0 0 0 0 1 0 0 105
106 0 0 0 0 0 0 0 0 1 0 106
107 0 0 0 0 0 0 0 0 0 1 107
108 0 0 0 0 0 0 0 0 0 0 108
109 0 0 0 0 0 0 0 0 0 0 109
110 1 0 0 0 0 0 0 0 0 0 110
111 0 1 0 0 0 0 0 0 0 0 111
112 0 0 1 0 0 0 0 0 0 0 112
113 0 0 0 1 0 0 0 0 0 0 113
114 0 0 0 0 1 0 0 0 0 0 114
115 0 0 0 0 0 1 0 0 0 0 115
116 0 0 0 0 0 0 1 0 0 0 116
117 0 0 0 0 0 0 0 1 0 0 117
118 0 0 0 0 0 0 0 0 1 0 118
119 0 0 0 0 0 0 0 0 0 1 119
120 0 0 0 0 0 0 0 0 0 0 120
121 0 0 0 0 0 0 0 0 0 0 121
122 1 0 0 0 0 0 0 0 0 0 122
123 0 1 0 0 0 0 0 0 0 0 123
124 0 0 1 0 0 0 0 0 0 0 124
125 0 0 0 1 0 0 0 0 0 0 125
126 0 0 0 0 1 0 0 0 0 0 126
127 0 0 0 0 0 1 0 0 0 0 127
128 0 0 0 0 0 0 1 0 0 0 128
129 0 0 0 0 0 0 0 1 0 0 129
130 0 0 0 0 0 0 0 0 1 0 130
131 0 0 0 0 0 0 0 0 0 1 131
132 0 0 0 0 0 0 0 0 0 0 132
133 0 0 0 0 0 0 0 0 0 0 133
134 1 0 0 0 0 0 0 0 0 0 134
135 0 1 0 0 0 0 0 0 0 0 135
136 0 0 1 0 0 0 0 0 0 0 136
137 0 0 0 1 0 0 0 0 0 0 137
138 0 0 0 0 1 0 0 0 0 0 138
139 0 0 0 0 0 1 0 0 0 0 139
140 0 0 0 0 0 0 1 0 0 0 140
141 0 0 0 0 0 0 0 1 0 0 141
142 0 0 0 0 0 0 0 0 1 0 142
143 0 0 0 0 0 0 0 0 0 1 143
144 0 0 0 0 0 0 0 0 0 0 144
145 0 0 0 0 0 0 0 0 0 0 145
146 1 0 0 0 0 0 0 0 0 0 146
147 0 1 0 0 0 0 0 0 0 0 147
148 0 0 1 0 0 0 0 0 0 0 148
149 0 0 0 1 0 0 0 0 0 0 149
150 0 0 0 0 1 0 0 0 0 0 150
151 0 0 0 0 0 1 0 0 0 0 151
152 0 0 0 0 0 0 1 0 0 0 152
153 0 0 0 0 0 0 0 1 0 0 153
154 0 0 0 0 0 0 0 0 1 0 154
155 0 0 0 0 0 0 0 0 0 1 155
156 0 0 0 0 0 0 0 0 0 0 156
157 0 0 0 0 0 0 0 0 0 0 157
158 1 0 0 0 0 0 0 0 0 0 158
159 0 1 0 0 0 0 0 0 0 0 159
160 0 0 1 0 0 0 0 0 0 0 160
161 0 0 0 1 0 0 0 0 0 0 161
162 0 0 0 0 1 0 0 0 0 0 162
163 0 0 0 0 0 1 0 0 0 0 163
164 0 0 0 0 0 0 1 0 0 0 164
165 0 0 0 0 0 0 0 1 0 0 165
166 0 0 0 0 0 0 0 0 1 0 166
167 0 0 0 0 0 0 0 0 0 1 167
168 0 0 0 0 0 0 0 0 0 0 168
169 0 0 0 0 0 0 0 0 0 0 169
170 1 0 0 0 0 0 0 0 0 0 170
171 0 1 0 0 0 0 0 0 0 0 171
172 0 0 1 0 0 0 0 0 0 0 172
173 0 0 0 1 0 0 0 0 0 0 173
174 0 0 0 0 1 0 0 0 0 0 174
175 0 0 0 0 0 1 0 0 0 0 175
176 0 0 0 0 0 0 1 0 0 0 176
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-5.462e+02 4.978e-03 6.413e-02
Conjunct_Seizoenzuiver Cons_vertrouw Rend_oblig_EUR
-2.444e+00 -9.568e-01 3.790e+01
Alg_consumptie_index_BE Y1 Y2
1.192e+01 1.062e+00 -2.927e-01
Y3 Y4 M1
1.361e-01 -1.835e-02 -5.149e+01
M2 M3 M4
-5.679e+01 -5.803e+01 -3.777e+01
M5 M6 M7
-6.878e+01 -1.204e+02 -2.682e+00
M8 M9 M10
-5.997e+01 -8.575e+01 -6.767e+01
M11 t
-7.637e+01 2.218e-01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-292.73 -65.35 -24.11 42.96 479.56
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.462e+02 1.950e+02 -2.801 0.00574 **
Nikkei 4.978e-03 4.720e-03 1.055 0.29315
DJ_Indust 6.413e-02 1.423e-02 4.506 1.31e-05 ***
Conjunct_Seizoenzuiver -2.444e+00 2.172e+00 -1.125 0.26224
Cons_vertrouw -9.568e-01 2.022e+00 -0.473 0.63679
Rend_oblig_EUR 3.790e+01 1.551e+01 2.444 0.01565 *
Alg_consumptie_index_BE 1.192e+01 1.062e+01 1.122 0.26371
Y1 1.062e+00 8.112e-02 13.092 < 2e-16 ***
Y2 -2.927e-01 1.176e-01 -2.490 0.01385 *
Y3 1.361e-01 1.170e-01 1.163 0.24671
Y4 -1.835e-02 7.742e-02 -0.237 0.81298
M1 -5.149e+01 4.441e+01 -1.160 0.24805
M2 -5.679e+01 4.437e+01 -1.280 0.20259
M3 -5.803e+01 4.447e+01 -1.305 0.19391
M4 -3.777e+01 4.426e+01 -0.853 0.39479
M5 -6.878e+01 4.433e+01 -1.551 0.12287
M6 -1.204e+02 4.428e+01 -2.718 0.00733 **
M7 -2.682e+00 4.435e+01 -0.060 0.95187
M8 -5.997e+01 4.473e+01 -1.341 0.18198
M9 -8.575e+01 4.528e+01 -1.894 0.06018 .
M10 -6.767e+01 4.507e+01 -1.501 0.13532
M11 -7.637e+01 4.484e+01 -1.703 0.09054 .
t 2.218e-01 4.606e-01 0.482 0.63077
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 118 on 153 degrees of freedom
Multiple R-squared: 0.9821, Adjusted R-squared: 0.9795
F-statistic: 381.9 on 22 and 153 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,] 0.23336567 4.667313e-01 7.666343e-01
[2,] 0.36964286 7.392857e-01 6.303571e-01
[3,] 0.53175055 9.364989e-01 4.682494e-01
[4,] 0.40656511 8.131302e-01 5.934349e-01
[5,] 0.29960649 5.992130e-01 7.003935e-01
[6,] 0.22199488 4.439898e-01 7.780051e-01
[7,] 0.16962746 3.392549e-01 8.303725e-01
[8,] 0.13492894 2.698579e-01 8.650711e-01
[9,] 0.11383734 2.276747e-01 8.861627e-01
[10,] 0.08575634 1.715127e-01 9.142437e-01
[11,] 0.05950296 1.190059e-01 9.404970e-01
[12,] 0.03774675 7.549350e-02 9.622532e-01
[13,] 0.03417164 6.834327e-02 9.658284e-01
[14,] 0.07959774 1.591955e-01 9.204023e-01
[15,] 0.14553583 2.910717e-01 8.544642e-01
[16,] 0.11568235 2.313647e-01 8.843176e-01
[17,] 0.14779965 2.955993e-01 8.522004e-01
[18,] 0.11151169 2.230234e-01 8.884883e-01
[19,] 0.11268234 2.253647e-01 8.873177e-01
[20,] 0.10530359 2.106072e-01 8.946964e-01
[21,] 0.07944124 1.588825e-01 9.205588e-01
[22,] 0.10349843 2.069969e-01 8.965016e-01
[23,] 0.10965986 2.193197e-01 8.903401e-01
[24,] 0.12515943 2.503189e-01 8.748406e-01
[25,] 0.09705589 1.941118e-01 9.029441e-01
[26,] 0.07581583 1.516317e-01 9.241842e-01
[27,] 0.06990344 1.398069e-01 9.300966e-01
[28,] 0.06469816 1.293963e-01 9.353018e-01
[29,] 0.09823993 1.964799e-01 9.017601e-01
[30,] 0.08501431 1.700286e-01 9.149857e-01
[31,] 0.06554700 1.310940e-01 9.344530e-01
[32,] 0.05434109 1.086822e-01 9.456589e-01
[33,] 0.04615368 9.230737e-02 9.538463e-01
[34,] 0.04794146 9.588292e-02 9.520585e-01
[35,] 0.06498581 1.299716e-01 9.350142e-01
[36,] 0.08955558 1.791112e-01 9.104444e-01
[37,] 0.12394129 2.478826e-01 8.760587e-01
[38,] 0.19848402 3.969680e-01 8.015160e-01
[39,] 0.24069636 4.813927e-01 7.593036e-01
[40,] 0.27234911 5.446982e-01 7.276509e-01
[41,] 0.24809480 4.961896e-01 7.519052e-01
[42,] 0.20977935 4.195587e-01 7.902206e-01
[43,] 0.21796567 4.359313e-01 7.820343e-01
[44,] 0.19570734 3.914147e-01 8.042927e-01
[45,] 0.16203319 3.240664e-01 8.379668e-01
[46,] 0.15565817 3.113163e-01 8.443418e-01
[47,] 0.15364561 3.072912e-01 8.463544e-01
[48,] 0.14253597 2.850719e-01 8.574640e-01
[49,] 0.13524083 2.704817e-01 8.647592e-01
[50,] 0.11489812 2.297962e-01 8.851019e-01
[51,] 0.14047746 2.809549e-01 8.595225e-01
[52,] 0.15423897 3.084779e-01 8.457610e-01
[53,] 0.16884738 3.376948e-01 8.311526e-01
[54,] 0.26576741 5.315348e-01 7.342326e-01
[55,] 0.51838579 9.632284e-01 4.816142e-01
[56,] 0.69761491 6.047702e-01 3.023851e-01
[57,] 0.76282930 4.743414e-01 2.371707e-01
[58,] 0.77342412 4.531518e-01 2.265759e-01
[59,] 0.78778044 4.244391e-01 2.122196e-01
[60,] 0.80578106 3.884379e-01 1.942189e-01
[61,] 0.80105325 3.978935e-01 1.989467e-01
[62,] 0.80460144 3.907971e-01 1.953986e-01
[63,] 0.79423199 4.115360e-01 2.057680e-01
[64,] 0.77815361 4.436928e-01 2.218464e-01
[65,] 0.81577607 3.684479e-01 1.842239e-01
[66,] 0.80059746 3.988051e-01 1.994025e-01
[67,] 0.78005936 4.398813e-01 2.199406e-01
[68,] 0.76026454 4.794709e-01 2.397355e-01
[69,] 0.72410624 5.517875e-01 2.758938e-01
[70,] 0.69196739 6.160652e-01 3.080326e-01
[71,] 0.68022163 6.395567e-01 3.197784e-01
[72,] 0.72079272 5.584146e-01 2.792073e-01
[73,] 0.72782810 5.443438e-01 2.721719e-01
[74,] 0.78968604 4.206279e-01 2.103140e-01
[75,] 0.87698098 2.460380e-01 1.230190e-01
[76,] 0.86155791 2.768842e-01 1.384421e-01
[77,] 0.83704967 3.259007e-01 1.629503e-01
[78,] 0.81481147 3.703771e-01 1.851885e-01
[79,] 0.82330492 3.533902e-01 1.766951e-01
[80,] 0.78976769 4.204646e-01 2.102323e-01
[81,] 0.78739666 4.252067e-01 2.126033e-01
[82,] 0.78840841 4.231832e-01 2.115916e-01
[83,] 0.80206340 3.958732e-01 1.979366e-01
[84,] 0.79777984 4.044403e-01 2.022202e-01
[85,] 0.83801477 3.239705e-01 1.619852e-01
[86,] 0.83640740 3.271852e-01 1.635926e-01
[87,] 0.83160781 3.367844e-01 1.683922e-01
[88,] 0.81531113 3.693777e-01 1.846889e-01
[89,] 0.85520770 2.895846e-01 1.447923e-01
[90,] 0.84604172 3.079166e-01 1.539583e-01
[91,] 0.82792754 3.441449e-01 1.720725e-01
[92,] 0.83033514 3.393297e-01 1.696649e-01
[93,] 0.80047803 3.990439e-01 1.995220e-01
[94,] 0.87839480 2.432104e-01 1.216052e-01
[95,] 0.89938420 2.012316e-01 1.006158e-01
[96,] 0.96746980 6.506039e-02 3.253020e-02
[97,] 0.95924205 8.151590e-02 4.075795e-02
[98,] 0.94490784 1.101843e-01 5.509216e-02
[99,] 0.94214045 1.157191e-01 5.785955e-02
[100,] 0.93642787 1.271443e-01 6.357213e-02
[101,] 0.93768177 1.246365e-01 6.231823e-02
[102,] 0.94503394 1.099321e-01 5.496606e-02
[103,] 0.94452522 1.109496e-01 5.547478e-02
[104,] 0.92493744 1.501251e-01 7.506256e-02
[105,] 0.90242228 1.951554e-01 9.757772e-02
[106,] 0.93069381 1.386124e-01 6.930619e-02
[107,] 0.97443039 5.113922e-02 2.556961e-02
[108,] 0.99692843 6.143141e-03 3.071571e-03
[109,] 0.99997546 4.907487e-05 2.453743e-05
[110,] 0.99999984 3.207619e-07 1.603810e-07
[111,] 1.00000000 5.403143e-09 2.701571e-09
[112,] 0.99999999 1.491193e-08 7.455965e-09
[113,] 0.99999998 4.798840e-08 2.399420e-08
[114,] 0.99999994 1.169187e-07 5.845936e-08
[115,] 0.99999991 1.886087e-07 9.430437e-08
[116,] 0.99999973 5.354164e-07 2.677082e-07
[117,] 0.99999878 2.444508e-06 1.222254e-06
[118,] 0.99999739 5.225321e-06 2.612661e-06
[119,] 0.99999104 1.792967e-05 8.964833e-06
[120,] 0.99997808 4.384809e-05 2.192404e-05
[121,] 0.99998130 3.740196e-05 1.870098e-05
[122,] 0.99991753 1.649448e-04 8.247242e-05
[123,] 0.99988660 2.268089e-04 1.134045e-04
[124,] 0.99993909 1.218156e-04 6.090779e-05
[125,] 0.99927328 1.453437e-03 7.267186e-04
> postscript(file="/var/www/rcomp/tmp/1oak11291585128.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2oak11291585128.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3oak11291585128.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/4hjj41291585128.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/5hjj41291585128.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 = 176
Frequency = 1
1 2 3 4 5 6
-81.5104472 -131.7437147 16.3441479 13.8479619 -147.4388763 -38.5648655
7 8 9 10 11 12
86.9159692 14.3124685 -77.1013891 94.6609456 50.5686193 479.5583429
13 14 15 16 17 18
-177.0703752 -84.6538617 284.3188927 42.2101500 -16.3362007 -130.0976407
19 20 21 22 23 24
-31.2429253 27.8445727 161.7660859 -104.1786420 271.4767186 -292.7315707
25 26 27 28 29 30
-61.3069492 279.5031846 -27.2080491 94.6321189 -54.9754470 -71.7408503
31 32 33 34 35 36
139.5476527 -56.5173981 -33.4197899 3.9302194 31.9474888 -129.8696471
37 38 39 40 41 42
-2.4352467 -70.0661145 -41.1352068 238.2612487 108.3116045 181.4774150
43 44 45 46 47 48
-59.3633858 -31.1097455 -57.4990103 -39.1726771 45.1920245 -16.0137584
49 50 51 52 53 54
22.3478397 -43.6608776 0.3490829 -8.0557763 115.7207513 59.4727579
55 56 57 58 59 60
-40.1726374 -49.3233820 3.7276551 -37.6987823 -27.0614812 -132.6698533
61 62 63 64 65 66
-119.0266035 -43.1882821 -34.7628504 -90.7887480 48.8448412 -26.8861621
67 68 69 70 71 72
-40.4322021 -137.6429381 -133.3162111 -26.3516391 -84.7300832 -100.0822040
73 74 75 76 77 78
-65.3098272 -104.2599227 -6.0565334 -48.7286129 -40.2511679 -97.5894607
79 80 81 82 83 84
134.0258299 145.6995247 78.9832741 82.3299229 -37.1973940 123.6085718
85 86 87 88 89 90
109.5306669 96.1374148 221.4466700 135.6974506 -32.1723466 -63.2610538
91 92 93 94 95 96
-68.5836295 -71.7868220 -91.9737483 0.2375866 -22.2684047 -74.9999202
97 98 99 100 101 102
208.9045528 -91.1224633 -120.4987184 -145.9380530 4.8665099 18.5669915
103 104 105 106 107 108
49.7535854 -74.1719566 29.4778938 34.1597005 -95.8925784 -137.1024704
109 110 111 112 113 114
12.8689663 -202.9416941 -127.7898279 -115.8581566 -65.4846936 -119.2787341
115 116 117 118 119 120
-80.5672200 86.3076620 99.1544726 17.0613470 -222.0970720 121.6425295
121 122 123 124 125 126
-241.2206073 35.6350208 32.4982246 -7.8572182 151.9162698 124.2091821
127 128 129 130 131 132
194.4467780 186.7053663 54.8973752 36.3266634 -33.7207341 275.4535596
133 134 135 136 137 138
335.9134332 249.6350933 -96.1082057 -57.0584863 13.9670814 -33.1288884
139 140 141 142 143 144
-151.6907421 -27.9167378 38.2041605 -29.9453380 116.0837192 -56.3210127
145 146 147 148 149 150
26.2206175 113.0260600 -167.4370369 -42.4228788 -32.8365023 115.9223024
151 152 153 154 155 156
-73.9763069 -44.1959112 -30.4277296 17.7524879 -9.8013272 -61.2008653
157 158 159 160 161 162
14.3349282 -25.9575320 48.4235490 -44.8051412 -13.6622030 29.1522219
163 164 165 166 167 168
-90.7660243 -10.3680514 -42.4730389 -49.1117948 17.5005045 0.7282983
169 170 171 172 173 174
17.7590517 23.6576894 17.6158616 36.8641412 -40.4696207 51.7467848
175 176
32.1052582 42.1633484
> postscript(file="/var/www/rcomp/tmp/6hjj41291585128.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 = 176
Frequency = 1
lag(myerror, k = 1) myerror
0 -81.5104472 NA
1 -131.7437147 -81.5104472
2 16.3441479 -131.7437147
3 13.8479619 16.3441479
4 -147.4388763 13.8479619
5 -38.5648655 -147.4388763
6 86.9159692 -38.5648655
7 14.3124685 86.9159692
8 -77.1013891 14.3124685
9 94.6609456 -77.1013891
10 50.5686193 94.6609456
11 479.5583429 50.5686193
12 -177.0703752 479.5583429
13 -84.6538617 -177.0703752
14 284.3188927 -84.6538617
15 42.2101500 284.3188927
16 -16.3362007 42.2101500
17 -130.0976407 -16.3362007
18 -31.2429253 -130.0976407
19 27.8445727 -31.2429253
20 161.7660859 27.8445727
21 -104.1786420 161.7660859
22 271.4767186 -104.1786420
23 -292.7315707 271.4767186
24 -61.3069492 -292.7315707
25 279.5031846 -61.3069492
26 -27.2080491 279.5031846
27 94.6321189 -27.2080491
28 -54.9754470 94.6321189
29 -71.7408503 -54.9754470
30 139.5476527 -71.7408503
31 -56.5173981 139.5476527
32 -33.4197899 -56.5173981
33 3.9302194 -33.4197899
34 31.9474888 3.9302194
35 -129.8696471 31.9474888
36 -2.4352467 -129.8696471
37 -70.0661145 -2.4352467
38 -41.1352068 -70.0661145
39 238.2612487 -41.1352068
40 108.3116045 238.2612487
41 181.4774150 108.3116045
42 -59.3633858 181.4774150
43 -31.1097455 -59.3633858
44 -57.4990103 -31.1097455
45 -39.1726771 -57.4990103
46 45.1920245 -39.1726771
47 -16.0137584 45.1920245
48 22.3478397 -16.0137584
49 -43.6608776 22.3478397
50 0.3490829 -43.6608776
51 -8.0557763 0.3490829
52 115.7207513 -8.0557763
53 59.4727579 115.7207513
54 -40.1726374 59.4727579
55 -49.3233820 -40.1726374
56 3.7276551 -49.3233820
57 -37.6987823 3.7276551
58 -27.0614812 -37.6987823
59 -132.6698533 -27.0614812
60 -119.0266035 -132.6698533
61 -43.1882821 -119.0266035
62 -34.7628504 -43.1882821
63 -90.7887480 -34.7628504
64 48.8448412 -90.7887480
65 -26.8861621 48.8448412
66 -40.4322021 -26.8861621
67 -137.6429381 -40.4322021
68 -133.3162111 -137.6429381
69 -26.3516391 -133.3162111
70 -84.7300832 -26.3516391
71 -100.0822040 -84.7300832
72 -65.3098272 -100.0822040
73 -104.2599227 -65.3098272
74 -6.0565334 -104.2599227
75 -48.7286129 -6.0565334
76 -40.2511679 -48.7286129
77 -97.5894607 -40.2511679
78 134.0258299 -97.5894607
79 145.6995247 134.0258299
80 78.9832741 145.6995247
81 82.3299229 78.9832741
82 -37.1973940 82.3299229
83 123.6085718 -37.1973940
84 109.5306669 123.6085718
85 96.1374148 109.5306669
86 221.4466700 96.1374148
87 135.6974506 221.4466700
88 -32.1723466 135.6974506
89 -63.2610538 -32.1723466
90 -68.5836295 -63.2610538
91 -71.7868220 -68.5836295
92 -91.9737483 -71.7868220
93 0.2375866 -91.9737483
94 -22.2684047 0.2375866
95 -74.9999202 -22.2684047
96 208.9045528 -74.9999202
97 -91.1224633 208.9045528
98 -120.4987184 -91.1224633
99 -145.9380530 -120.4987184
100 4.8665099 -145.9380530
101 18.5669915 4.8665099
102 49.7535854 18.5669915
103 -74.1719566 49.7535854
104 29.4778938 -74.1719566
105 34.1597005 29.4778938
106 -95.8925784 34.1597005
107 -137.1024704 -95.8925784
108 12.8689663 -137.1024704
109 -202.9416941 12.8689663
110 -127.7898279 -202.9416941
111 -115.8581566 -127.7898279
112 -65.4846936 -115.8581566
113 -119.2787341 -65.4846936
114 -80.5672200 -119.2787341
115 86.3076620 -80.5672200
116 99.1544726 86.3076620
117 17.0613470 99.1544726
118 -222.0970720 17.0613470
119 121.6425295 -222.0970720
120 -241.2206073 121.6425295
121 35.6350208 -241.2206073
122 32.4982246 35.6350208
123 -7.8572182 32.4982246
124 151.9162698 -7.8572182
125 124.2091821 151.9162698
126 194.4467780 124.2091821
127 186.7053663 194.4467780
128 54.8973752 186.7053663
129 36.3266634 54.8973752
130 -33.7207341 36.3266634
131 275.4535596 -33.7207341
132 335.9134332 275.4535596
133 249.6350933 335.9134332
134 -96.1082057 249.6350933
135 -57.0584863 -96.1082057
136 13.9670814 -57.0584863
137 -33.1288884 13.9670814
138 -151.6907421 -33.1288884
139 -27.9167378 -151.6907421
140 38.2041605 -27.9167378
141 -29.9453380 38.2041605
142 116.0837192 -29.9453380
143 -56.3210127 116.0837192
144 26.2206175 -56.3210127
145 113.0260600 26.2206175
146 -167.4370369 113.0260600
147 -42.4228788 -167.4370369
148 -32.8365023 -42.4228788
149 115.9223024 -32.8365023
150 -73.9763069 115.9223024
151 -44.1959112 -73.9763069
152 -30.4277296 -44.1959112
153 17.7524879 -30.4277296
154 -9.8013272 17.7524879
155 -61.2008653 -9.8013272
156 14.3349282 -61.2008653
157 -25.9575320 14.3349282
158 48.4235490 -25.9575320
159 -44.8051412 48.4235490
160 -13.6622030 -44.8051412
161 29.1522219 -13.6622030
162 -90.7660243 29.1522219
163 -10.3680514 -90.7660243
164 -42.4730389 -10.3680514
165 -49.1117948 -42.4730389
166 17.5005045 -49.1117948
167 0.7282983 17.5005045
168 17.7590517 0.7282983
169 23.6576894 17.7590517
170 17.6158616 23.6576894
171 36.8641412 17.6158616
172 -40.4696207 36.8641412
173 51.7467848 -40.4696207
174 32.1052582 51.7467848
175 42.1633484 32.1052582
176 NA 42.1633484
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -131.7437147 -81.5104472
[2,] 16.3441479 -131.7437147
[3,] 13.8479619 16.3441479
[4,] -147.4388763 13.8479619
[5,] -38.5648655 -147.4388763
[6,] 86.9159692 -38.5648655
[7,] 14.3124685 86.9159692
[8,] -77.1013891 14.3124685
[9,] 94.6609456 -77.1013891
[10,] 50.5686193 94.6609456
[11,] 479.5583429 50.5686193
[12,] -177.0703752 479.5583429
[13,] -84.6538617 -177.0703752
[14,] 284.3188927 -84.6538617
[15,] 42.2101500 284.3188927
[16,] -16.3362007 42.2101500
[17,] -130.0976407 -16.3362007
[18,] -31.2429253 -130.0976407
[19,] 27.8445727 -31.2429253
[20,] 161.7660859 27.8445727
[21,] -104.1786420 161.7660859
[22,] 271.4767186 -104.1786420
[23,] -292.7315707 271.4767186
[24,] -61.3069492 -292.7315707
[25,] 279.5031846 -61.3069492
[26,] -27.2080491 279.5031846
[27,] 94.6321189 -27.2080491
[28,] -54.9754470 94.6321189
[29,] -71.7408503 -54.9754470
[30,] 139.5476527 -71.7408503
[31,] -56.5173981 139.5476527
[32,] -33.4197899 -56.5173981
[33,] 3.9302194 -33.4197899
[34,] 31.9474888 3.9302194
[35,] -129.8696471 31.9474888
[36,] -2.4352467 -129.8696471
[37,] -70.0661145 -2.4352467
[38,] -41.1352068 -70.0661145
[39,] 238.2612487 -41.1352068
[40,] 108.3116045 238.2612487
[41,] 181.4774150 108.3116045
[42,] -59.3633858 181.4774150
[43,] -31.1097455 -59.3633858
[44,] -57.4990103 -31.1097455
[45,] -39.1726771 -57.4990103
[46,] 45.1920245 -39.1726771
[47,] -16.0137584 45.1920245
[48,] 22.3478397 -16.0137584
[49,] -43.6608776 22.3478397
[50,] 0.3490829 -43.6608776
[51,] -8.0557763 0.3490829
[52,] 115.7207513 -8.0557763
[53,] 59.4727579 115.7207513
[54,] -40.1726374 59.4727579
[55,] -49.3233820 -40.1726374
[56,] 3.7276551 -49.3233820
[57,] -37.6987823 3.7276551
[58,] -27.0614812 -37.6987823
[59,] -132.6698533 -27.0614812
[60,] -119.0266035 -132.6698533
[61,] -43.1882821 -119.0266035
[62,] -34.7628504 -43.1882821
[63,] -90.7887480 -34.7628504
[64,] 48.8448412 -90.7887480
[65,] -26.8861621 48.8448412
[66,] -40.4322021 -26.8861621
[67,] -137.6429381 -40.4322021
[68,] -133.3162111 -137.6429381
[69,] -26.3516391 -133.3162111
[70,] -84.7300832 -26.3516391
[71,] -100.0822040 -84.7300832
[72,] -65.3098272 -100.0822040
[73,] -104.2599227 -65.3098272
[74,] -6.0565334 -104.2599227
[75,] -48.7286129 -6.0565334
[76,] -40.2511679 -48.7286129
[77,] -97.5894607 -40.2511679
[78,] 134.0258299 -97.5894607
[79,] 145.6995247 134.0258299
[80,] 78.9832741 145.6995247
[81,] 82.3299229 78.9832741
[82,] -37.1973940 82.3299229
[83,] 123.6085718 -37.1973940
[84,] 109.5306669 123.6085718
[85,] 96.1374148 109.5306669
[86,] 221.4466700 96.1374148
[87,] 135.6974506 221.4466700
[88,] -32.1723466 135.6974506
[89,] -63.2610538 -32.1723466
[90,] -68.5836295 -63.2610538
[91,] -71.7868220 -68.5836295
[92,] -91.9737483 -71.7868220
[93,] 0.2375866 -91.9737483
[94,] -22.2684047 0.2375866
[95,] -74.9999202 -22.2684047
[96,] 208.9045528 -74.9999202
[97,] -91.1224633 208.9045528
[98,] -120.4987184 -91.1224633
[99,] -145.9380530 -120.4987184
[100,] 4.8665099 -145.9380530
[101,] 18.5669915 4.8665099
[102,] 49.7535854 18.5669915
[103,] -74.1719566 49.7535854
[104,] 29.4778938 -74.1719566
[105,] 34.1597005 29.4778938
[106,] -95.8925784 34.1597005
[107,] -137.1024704 -95.8925784
[108,] 12.8689663 -137.1024704
[109,] -202.9416941 12.8689663
[110,] -127.7898279 -202.9416941
[111,] -115.8581566 -127.7898279
[112,] -65.4846936 -115.8581566
[113,] -119.2787341 -65.4846936
[114,] -80.5672200 -119.2787341
[115,] 86.3076620 -80.5672200
[116,] 99.1544726 86.3076620
[117,] 17.0613470 99.1544726
[118,] -222.0970720 17.0613470
[119,] 121.6425295 -222.0970720
[120,] -241.2206073 121.6425295
[121,] 35.6350208 -241.2206073
[122,] 32.4982246 35.6350208
[123,] -7.8572182 32.4982246
[124,] 151.9162698 -7.8572182
[125,] 124.2091821 151.9162698
[126,] 194.4467780 124.2091821
[127,] 186.7053663 194.4467780
[128,] 54.8973752 186.7053663
[129,] 36.3266634 54.8973752
[130,] -33.7207341 36.3266634
[131,] 275.4535596 -33.7207341
[132,] 335.9134332 275.4535596
[133,] 249.6350933 335.9134332
[134,] -96.1082057 249.6350933
[135,] -57.0584863 -96.1082057
[136,] 13.9670814 -57.0584863
[137,] -33.1288884 13.9670814
[138,] -151.6907421 -33.1288884
[139,] -27.9167378 -151.6907421
[140,] 38.2041605 -27.9167378
[141,] -29.9453380 38.2041605
[142,] 116.0837192 -29.9453380
[143,] -56.3210127 116.0837192
[144,] 26.2206175 -56.3210127
[145,] 113.0260600 26.2206175
[146,] -167.4370369 113.0260600
[147,] -42.4228788 -167.4370369
[148,] -32.8365023 -42.4228788
[149,] 115.9223024 -32.8365023
[150,] -73.9763069 115.9223024
[151,] -44.1959112 -73.9763069
[152,] -30.4277296 -44.1959112
[153,] 17.7524879 -30.4277296
[154,] -9.8013272 17.7524879
[155,] -61.2008653 -9.8013272
[156,] 14.3349282 -61.2008653
[157,] -25.9575320 14.3349282
[158,] 48.4235490 -25.9575320
[159,] -44.8051412 48.4235490
[160,] -13.6622030 -44.8051412
[161,] 29.1522219 -13.6622030
[162,] -90.7660243 29.1522219
[163,] -10.3680514 -90.7660243
[164,] -42.4730389 -10.3680514
[165,] -49.1117948 -42.4730389
[166,] 17.5005045 -49.1117948
[167,] 0.7282983 17.5005045
[168,] 17.7590517 0.7282983
[169,] 23.6576894 17.7590517
[170,] 17.6158616 23.6576894
[171,] 36.8641412 17.6158616
[172,] -40.4696207 36.8641412
[173,] 51.7467848 -40.4696207
[174,] 32.1052582 51.7467848
[175,] 42.1633484 32.1052582
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -131.7437147 -81.5104472
2 16.3441479 -131.7437147
3 13.8479619 16.3441479
4 -147.4388763 13.8479619
5 -38.5648655 -147.4388763
6 86.9159692 -38.5648655
7 14.3124685 86.9159692
8 -77.1013891 14.3124685
9 94.6609456 -77.1013891
10 50.5686193 94.6609456
11 479.5583429 50.5686193
12 -177.0703752 479.5583429
13 -84.6538617 -177.0703752
14 284.3188927 -84.6538617
15 42.2101500 284.3188927
16 -16.3362007 42.2101500
17 -130.0976407 -16.3362007
18 -31.2429253 -130.0976407
19 27.8445727 -31.2429253
20 161.7660859 27.8445727
21 -104.1786420 161.7660859
22 271.4767186 -104.1786420
23 -292.7315707 271.4767186
24 -61.3069492 -292.7315707
25 279.5031846 -61.3069492
26 -27.2080491 279.5031846
27 94.6321189 -27.2080491
28 -54.9754470 94.6321189
29 -71.7408503 -54.9754470
30 139.5476527 -71.7408503
31 -56.5173981 139.5476527
32 -33.4197899 -56.5173981
33 3.9302194 -33.4197899
34 31.9474888 3.9302194
35 -129.8696471 31.9474888
36 -2.4352467 -129.8696471
37 -70.0661145 -2.4352467
38 -41.1352068 -70.0661145
39 238.2612487 -41.1352068
40 108.3116045 238.2612487
41 181.4774150 108.3116045
42 -59.3633858 181.4774150
43 -31.1097455 -59.3633858
44 -57.4990103 -31.1097455
45 -39.1726771 -57.4990103
46 45.1920245 -39.1726771
47 -16.0137584 45.1920245
48 22.3478397 -16.0137584
49 -43.6608776 22.3478397
50 0.3490829 -43.6608776
51 -8.0557763 0.3490829
52 115.7207513 -8.0557763
53 59.4727579 115.7207513
54 -40.1726374 59.4727579
55 -49.3233820 -40.1726374
56 3.7276551 -49.3233820
57 -37.6987823 3.7276551
58 -27.0614812 -37.6987823
59 -132.6698533 -27.0614812
60 -119.0266035 -132.6698533
61 -43.1882821 -119.0266035
62 -34.7628504 -43.1882821
63 -90.7887480 -34.7628504
64 48.8448412 -90.7887480
65 -26.8861621 48.8448412
66 -40.4322021 -26.8861621
67 -137.6429381 -40.4322021
68 -133.3162111 -137.6429381
69 -26.3516391 -133.3162111
70 -84.7300832 -26.3516391
71 -100.0822040 -84.7300832
72 -65.3098272 -100.0822040
73 -104.2599227 -65.3098272
74 -6.0565334 -104.2599227
75 -48.7286129 -6.0565334
76 -40.2511679 -48.7286129
77 -97.5894607 -40.2511679
78 134.0258299 -97.5894607
79 145.6995247 134.0258299
80 78.9832741 145.6995247
81 82.3299229 78.9832741
82 -37.1973940 82.3299229
83 123.6085718 -37.1973940
84 109.5306669 123.6085718
85 96.1374148 109.5306669
86 221.4466700 96.1374148
87 135.6974506 221.4466700
88 -32.1723466 135.6974506
89 -63.2610538 -32.1723466
90 -68.5836295 -63.2610538
91 -71.7868220 -68.5836295
92 -91.9737483 -71.7868220
93 0.2375866 -91.9737483
94 -22.2684047 0.2375866
95 -74.9999202 -22.2684047
96 208.9045528 -74.9999202
97 -91.1224633 208.9045528
98 -120.4987184 -91.1224633
99 -145.9380530 -120.4987184
100 4.8665099 -145.9380530
101 18.5669915 4.8665099
102 49.7535854 18.5669915
103 -74.1719566 49.7535854
104 29.4778938 -74.1719566
105 34.1597005 29.4778938
106 -95.8925784 34.1597005
107 -137.1024704 -95.8925784
108 12.8689663 -137.1024704
109 -202.9416941 12.8689663
110 -127.7898279 -202.9416941
111 -115.8581566 -127.7898279
112 -65.4846936 -115.8581566
113 -119.2787341 -65.4846936
114 -80.5672200 -119.2787341
115 86.3076620 -80.5672200
116 99.1544726 86.3076620
117 17.0613470 99.1544726
118 -222.0970720 17.0613470
119 121.6425295 -222.0970720
120 -241.2206073 121.6425295
121 35.6350208 -241.2206073
122 32.4982246 35.6350208
123 -7.8572182 32.4982246
124 151.9162698 -7.8572182
125 124.2091821 151.9162698
126 194.4467780 124.2091821
127 186.7053663 194.4467780
128 54.8973752 186.7053663
129 36.3266634 54.8973752
130 -33.7207341 36.3266634
131 275.4535596 -33.7207341
132 335.9134332 275.4535596
133 249.6350933 335.9134332
134 -96.1082057 249.6350933
135 -57.0584863 -96.1082057
136 13.9670814 -57.0584863
137 -33.1288884 13.9670814
138 -151.6907421 -33.1288884
139 -27.9167378 -151.6907421
140 38.2041605 -27.9167378
141 -29.9453380 38.2041605
142 116.0837192 -29.9453380
143 -56.3210127 116.0837192
144 26.2206175 -56.3210127
145 113.0260600 26.2206175
146 -167.4370369 113.0260600
147 -42.4228788 -167.4370369
148 -32.8365023 -42.4228788
149 115.9223024 -32.8365023
150 -73.9763069 115.9223024
151 -44.1959112 -73.9763069
152 -30.4277296 -44.1959112
153 17.7524879 -30.4277296
154 -9.8013272 17.7524879
155 -61.2008653 -9.8013272
156 14.3349282 -61.2008653
157 -25.9575320 14.3349282
158 48.4235490 -25.9575320
159 -44.8051412 48.4235490
160 -13.6622030 -44.8051412
161 29.1522219 -13.6622030
162 -90.7660243 29.1522219
163 -10.3680514 -90.7660243
164 -42.4730389 -10.3680514
165 -49.1117948 -42.4730389
166 17.5005045 -49.1117948
167 0.7282983 17.5005045
168 17.7590517 0.7282983
169 23.6576894 17.7590517
170 17.6158616 23.6576894
171 36.8641412 17.6158616
172 -40.4696207 36.8641412
173 51.7467848 -40.4696207
174 32.1052582 51.7467848
175 42.1633484 32.1052582
> 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/7rsj71291585128.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8k2ia1291585128.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9k2ia1291585128.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10vbhd1291585128.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11ybg01291585128.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/12rlx41291585128.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/13ymcx1291585128.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/14j4b31291585128.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/15ceao1291585128.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/168nqf1291585128.tab")
+ }
>
> try(system("convert tmp/1oak11291585128.ps tmp/1oak11291585128.png",intern=TRUE))
character(0)
> try(system("convert tmp/2oak11291585128.ps tmp/2oak11291585128.png",intern=TRUE))
character(0)
> try(system("convert tmp/3oak11291585128.ps tmp/3oak11291585128.png",intern=TRUE))
character(0)
> try(system("convert tmp/4hjj41291585128.ps tmp/4hjj41291585128.png",intern=TRUE))
character(0)
> try(system("convert tmp/5hjj41291585128.ps tmp/5hjj41291585128.png",intern=TRUE))
character(0)
> try(system("convert tmp/6hjj41291585128.ps tmp/6hjj41291585128.png",intern=TRUE))
character(0)
> try(system("convert tmp/7rsj71291585128.ps tmp/7rsj71291585128.png",intern=TRUE))
character(0)
> try(system("convert tmp/8k2ia1291585128.ps tmp/8k2ia1291585128.png",intern=TRUE))
character(0)
> try(system("convert tmp/9k2ia1291585128.ps tmp/9k2ia1291585128.png",intern=TRUE))
character(0)
> try(system("convert tmp/10vbhd1291585128.ps tmp/10vbhd1291585128.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
6.180 1.710 7.899