R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(3484.74
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+ ,21467
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+ ,9634.97
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+ ,10066.24
+ ,9857.34
+ ,22680
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+ ,10433.44
+ ,24977
+ ,-7.9
+ ,-15
+ ,0.3
+ ,3.36)
+ ,dim=c(8
+ ,132)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_5j')
+ ,1:132))
> y <- array(NA,dim=c(8,132),dimnames=list(c('BEL_20','Nikkei','DJ_Indust','Goudprijs','Conjunct_Seizoenzuiver','Cons_vertrouw','Alg_consumptie_index_BE','Gem_rente_kasbon_5j'),1:132))
> 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
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> 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 3484.74 13830.14 9349.44 7977 -5.6 6
2 3411.13 14153.22 9327.78 8241 -6.2 3
3 3288.18 15418.03 9753.63 8444 -7.1 2
4 3280.37 16666.97 10443.50 8490 -1.4 2
5 3173.95 16505.21 10853.87 8388 -0.1 2
6 3165.26 17135.96 10704.02 8099 -0.9 -8
7 3092.71 18033.25 11052.23 7984 0.0 0
8 3053.05 17671.00 10935.47 7786 0.1 -2
9 3181.96 17544.22 10714.03 8086 2.6 3
10 2999.93 17677.90 10394.48 9315 6.0 5
11 3249.57 18470.97 10817.90 9113 6.4 8
12 3210.52 18409.96 11251.20 9023 8.6 8
13 3030.29 18941.60 11281.26 9026 6.4 9
14 2803.47 19685.53 10539.68 9787 7.7 11
15 2767.63 19834.71 10483.39 9536 9.2 13
16 2882.60 19598.93 10947.43 9490 8.6 12
17 2863.36 17039.97 10580.27 9736 7.4 13
18 2897.06 16969.28 10582.92 9694 8.6 15
19 3012.61 16973.38 10654.41 9647 6.2 13
20 3142.95 16329.89 11014.51 9753 6.0 16
21 3032.93 16153.34 10967.87 10070 6.6 10
22 3045.78 15311.70 10433.56 10137 5.1 14
23 3110.52 14760.87 10665.78 9984 4.7 14
24 3013.24 14452.93 10666.71 9732 5.0 15
25 2987.10 13720.95 10682.74 9103 3.6 13
26 2995.55 13266.27 10777.22 9155 1.9 8
27 2833.18 12708.47 10052.60 9308 -0.1 7
28 2848.96 13411.84 10213.97 9394 -5.7 3
29 2794.83 13975.55 10546.82 9948 -5.6 3
30 2845.26 12974.89 10767.20 10177 -6.4 4
31 2915.02 12151.11 10444.50 10002 -7.7 4
32 2892.63 11576.21 10314.68 9728 -8.0 0
33 2604.42 9996.83 9042.56 10002 -11.9 -4
34 2641.65 10438.90 9220.75 10063 -15.4 -14
35 2659.81 10511.22 9721.84 10018 -15.5 -18
36 2638.53 10496.20 9978.53 9960 -13.4 -8
37 2720.25 10300.79 9923.81 10236 -10.9 -1
38 2745.88 9981.65 9892.56 10893 -10.8 1
39 2735.70 11448.79 10500.98 10756 -7.3 2
40 2811.70 11384.49 10179.35 10940 -6.5 0
41 2799.43 11717.46 10080.48 10997 -5.1 1
42 2555.28 10965.88 9492.44 10827 -5.3 0
43 2304.98 10352.27 8616.49 10166 -6.8 -1
44 2214.95 9751.20 8685.40 10186 -8.4 -3
45 2065.81 9354.01 8160.67 10457 -8.4 -3
46 1940.49 8792.50 8048.10 10368 -9.7 -3
47 2042.00 8721.14 8641.21 10244 -8.8 -4
48 1995.37 8692.94 8526.63 10511 -9.6 -8
49 1946.81 8570.73 8474.21 10812 -11.5 -9
50 1765.90 8538.47 7916.13 10738 -11.0 -13
51 1635.25 8169.75 7977.64 10171 -14.9 -18
52 1833.42 7905.84 8334.59 9721 -16.2 -11
53 1910.43 8145.82 8623.36 9897 -14.4 -9
54 1959.67 8895.71 9098.03 9828 -17.3 -10
55 1969.60 9676.31 9154.34 9924 -15.7 -13
56 2061.41 9884.59 9284.73 10371 -12.6 -11
57 2093.48 10637.44 9492.49 10846 -9.4 -5
58 2120.88 10717.13 9682.35 10413 -8.1 -15
59 2174.56 10205.29 9762.12 10709 -5.4 -6
60 2196.72 10295.98 10124.63 10662 -4.6 -6
61 2350.44 10892.76 10540.05 10570 -4.9 -3
62 2440.25 10631.92 10601.61 10297 -4.0 -1
63 2408.64 11441.08 10323.73 10635 -3.1 -3
64 2472.81 11950.95 10418.40 10872 -1.3 -4
65 2407.60 11037.54 10092.96 10296 0.0 -6
66 2454.62 11527.72 10364.91 10383 -0.4 0
67 2448.05 11383.89 10152.09 10431 3.0 -4
68 2497.84 10989.34 10032.80 10574 0.4 -2
69 2645.64 11079.42 10204.59 10653 1.2 -2
70 2756.76 11028.93 10001.60 10805 0.6 -6
71 2849.27 10973.00 10411.75 10872 -1.3 -7
72 2921.44 11068.05 10673.38 10625 -3.2 -6
73 2981.85 11394.84 10539.51 10407 -1.8 -6
74 3080.58 11545.71 10723.78 10463 -3.6 -3
75 3106.22 11809.38 10682.06 10556 -4.2 -2
76 3119.31 11395.64 10283.19 10646 -6.9 -5
77 3061.26 11082.38 10377.18 10702 -8.0 -11
78 3097.31 11402.75 10486.64 11353 -7.5 -11
79 3161.69 11716.87 10545.38 11346 -8.2 -11
80 3257.16 12204.98 10554.27 11451 -7.6 -10
81 3277.01 12986.62 10532.54 11964 -3.7 -14
82 3295.32 13392.79 10324.31 12574 -1.7 -8
83 3363.99 14368.05 10695.25 13031 -0.7 -9
84 3494.17 15650.83 10827.81 13812 0.2 -5
85 3667.03 16102.64 10872.48 14544 0.6 -1
86 3813.06 16187.64 10971.19 14931 2.2 -2
87 3917.96 16311.54 11145.65 14886 3.3 -5
88 3895.51 17232.97 11234.68 16005 5.3 -4
89 3801.06 16397.83 11333.88 17064 5.5 -6
90 3570.12 14990.31 10997.97 15168 6.3 -2
91 3701.61 15147.55 11036.89 16050 7.7 -2
92 3862.27 15786.78 11257.35 15839 6.5 -2
93 3970.10 15934.09 11533.59 15137 5.5 -2
94 4138.52 16519.44 11963.12 14954 6.9 2
95 4199.75 16101.07 12185.15 15648 5.7 1
96 4290.89 16775.08 12377.62 15305 6.9 -8
97 4443.91 17286.32 12512.89 15579 6.1 -1
98 4502.64 17741.23 12631.48 16348 4.8 1
99 4356.98 17128.37 12268.53 15928 3.7 -1
100 4591.27 17460.53 12754.80 16171 5.8 2
101 4696.96 17611.14 13407.75 15937 6.8 2
102 4621.40 18001.37 13480.21 15713 8.5 1
103 4562.84 17974.77 13673.28 15594 7.2 -1
104 4202.52 16460.95 13239.71 15683 5.0 -2
105 4296.49 16235.39 13557.69 16438 4.7 -2
106 4435.23 16903.36 13901.28 17032 2.3 -1
107 4105.18 15543.76 13200.58 17696 2.4 -8
108 4116.68 15532.18 13406.97 17745 0.1 -4
109 3844.49 13731.31 12538.12 19394 1.9 -6
110 3720.98 13547.84 12419.57 20148 1.7 -3
111 3674.40 12602.93 12193.88 20108 2.0 -3
112 3857.62 13357.70 12656.63 18584 -1.9 -7
113 3801.06 13995.33 12812.48 18441 0.5 -9
114 3504.37 14084.60 12056.67 18391 -1.3 -11
115 3032.60 13168.91 11322.38 19178 -3.3 -13
116 3047.03 12989.35 11530.75 18079 -2.8 -11
117 2962.34 12123.53 11114.08 18483 -8.0 -9
118 2197.82 9117.03 9181.73 19644 -13.9 -17
119 2014.45 8531.45 8614.55 19195 -21.9 -22
120 1862.83 8460.94 8595.56 19650 -28.8 -25
121 1905.41 8331.49 8396.20 20830 -27.6 -20
122 1810.99 7694.78 7690.50 23595 -31.4 -24
123 1670.07 7764.58 7235.47 22937 -31.8 -24
124 1864.44 8767.96 7992.12 21814 -29.4 -22
125 2052.02 9304.43 8398.37 21928 -27.6 -19
126 2029.60 9810.31 8593.00 21777 -23.6 -18
127 2070.83 9691.12 8679.75 21383 -22.8 -17
128 2293.41 10430.35 9374.63 21467 -18.2 -11
129 2443.27 10302.87 9634.97 22052 -17.8 -11
130 2513.17 10066.24 9857.34 22680 -14.2 -12
131 2466.92 9633.83 10238.83 24320 -8.8 -10
132 2502.66 10169.02 10433.44 24977 -7.9 -15
Alg_consumptie_index_BE Gem_rente_kasbon_5j M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 1.0 3.17 1 0 0 0 0 0 0 0 0 0
2 1.0 3.17 0 1 0 0 0 0 0 0 0 0
3 1.2 3.36 0 0 1 0 0 0 0 0 0 0
4 1.2 3.11 0 0 0 1 0 0 0 0 0 0
5 0.8 3.11 0 0 0 0 1 0 0 0 0 0
6 0.7 3.57 0 0 0 0 0 1 0 0 0 0
7 0.7 4.04 0 0 0 0 0 0 1 0 0 0
8 0.9 4.21 0 0 0 0 0 0 0 1 0 0
9 1.2 4.36 0 0 0 0 0 0 0 0 1 0
10 1.3 4.75 0 0 0 0 0 0 0 0 0 1
11 1.5 4.43 0 0 0 0 0 0 0 0 0 0
12 1.9 4.70 0 0 0 0 0 0 0 0 0 0
13 1.8 4.81 1 0 0 0 0 0 0 0 0 0
14 1.9 5.01 0 1 0 0 0 0 0 0 0 0
15 2.2 5.00 0 0 1 0 0 0 0 0 0 0
16 2.1 4.81 0 0 0 1 0 0 0 0 0 0
17 2.2 5.11 0 0 0 0 1 0 0 0 0 0
18 2.7 5.10 0 0 0 0 0 1 0 0 0 0
19 2.8 5.11 0 0 0 0 0 0 1 0 0 0
20 2.9 5.21 0 0 0 0 0 0 0 1 0 0
21 3.4 5.21 0 0 0 0 0 0 0 0 1 0
22 3.0 5.21 0 0 0 0 0 0 0 0 0 1
23 3.1 5.06 0 0 0 0 0 0 0 0 0 0
24 2.5 4.58 0 0 0 0 0 0 0 0 0 0
25 2.2 4.37 1 0 0 0 0 0 0 0 0 0
26 2.3 4.37 0 1 0 0 0 0 0 0 0 0
27 2.1 4.23 0 0 1 0 0 0 0 0 0 0
28 2.8 4.23 0 0 0 1 0 0 0 0 0 0
29 3.1 4.37 0 0 0 0 1 0 0 0 0 0
30 2.9 4.31 0 0 0 0 0 1 0 0 0 0
31 2.6 4.31 0 0 0 0 0 0 1 0 0 0
32 2.7 4.28 0 0 0 0 0 0 0 1 0 0
33 2.3 3.98 0 0 0 0 0 0 0 0 1 0
34 2.3 3.79 0 0 0 0 0 0 0 0 0 1
35 2.1 3.55 0 0 0 0 0 0 0 0 0 0
36 2.2 4.00 0 0 0 0 0 0 0 0 0 0
37 2.9 4.02 1 0 0 0 0 0 0 0 0 0
38 2.6 4.21 0 1 0 0 0 0 0 0 0 0
39 2.7 4.50 0 0 1 0 0 0 0 0 0 0
40 1.8 4.52 0 0 0 1 0 0 0 0 0 0
41 1.3 4.45 0 0 0 0 1 0 0 0 0 0
42 0.9 4.28 0 0 0 0 0 1 0 0 0 0
43 1.3 4.08 0 0 0 0 0 0 1 0 0 0
44 1.3 3.80 0 0 0 0 0 0 0 1 0 0
45 1.3 3.58 0 0 0 0 0 0 0 0 1 0
46 1.3 3.58 0 0 0 0 0 0 0 0 0 1
47 1.1 3.58 0 0 0 0 0 0 0 0 0 0
48 1.4 3.54 0 0 0 0 0 0 0 0 0 0
49 1.2 3.19 1 0 0 0 0 0 0 0 0 0
50 1.7 2.91 0 1 0 0 0 0 0 0 0 0
51 1.8 2.87 0 0 1 0 0 0 0 0 0 0
52 1.5 3.10 0 0 0 1 0 0 0 0 0 0
53 1.0 2.60 0 0 0 0 1 0 0 0 0 0
54 1.6 2.33 0 0 0 0 0 1 0 0 0 0
55 1.5 2.62 0 0 0 0 0 0 1 0 0 0
56 1.8 3.05 0 0 0 0 0 0 0 1 0 0
57 1.8 3.05 0 0 0 0 0 0 0 0 1 0
58 1.6 3.22 0 0 0 0 0 0 0 0 0 1
59 1.9 3.24 0 0 0 0 0 0 0 0 0 0
60 1.7 3.24 0 0 0 0 0 0 0 0 0 0
61 1.6 3.38 1 0 0 0 0 0 0 0 0 0
62 1.3 3.35 0 1 0 0 0 0 0 0 0 0
63 1.1 3.22 0 0 1 0 0 0 0 0 0 0
64 1.9 3.06 0 0 0 1 0 0 0 0 0 0
65 2.6 3.17 0 0 0 0 1 0 0 0 0 0
66 2.3 3.19 0 0 0 0 0 1 0 0 0 0
67 2.4 3.35 0 0 0 0 0 0 1 0 0 0
68 2.2 3.24 0 0 0 0 0 0 0 1 0 0
69 2.0 3.23 0 0 0 0 0 0 0 0 1 0
70 2.9 3.31 0 0 0 0 0 0 0 0 0 1
71 2.6 3.25 0 0 0 0 0 0 0 0 0 0
72 2.3 3.20 0 0 0 0 0 0 0 0 0 0
73 2.3 3.10 1 0 0 0 0 0 0 0 0 0
74 2.6 2.93 0 1 0 0 0 0 0 0 0 0
75 3.1 2.92 0 0 1 0 0 0 0 0 0 0
76 2.8 2.90 0 0 0 1 0 0 0 0 0 0
77 2.5 2.87 0 0 0 0 1 0 0 0 0 0
78 2.9 2.76 0 0 0 0 0 1 0 0 0 0
79 3.1 2.67 0 0 0 0 0 0 1 0 0 0
80 3.1 2.75 0 0 0 0 0 0 0 1 0 0
81 3.2 2.72 0 0 0 0 0 0 0 0 1 0
82 2.5 2.72 0 0 0 0 0 0 0 0 0 1
83 2.6 2.86 0 0 0 0 0 0 0 0 0 0
84 2.9 2.99 0 0 0 0 0 0 0 0 0 0
85 2.6 3.07 1 0 0 0 0 0 0 0 0 0
86 2.4 2.96 0 1 0 0 0 0 0 0 0 0
87 1.7 3.04 0 0 1 0 0 0 0 0 0 0
88 2.0 3.30 0 0 0 1 0 0 0 0 0 0
89 2.2 3.48 0 0 0 0 1 0 0 0 0 0
90 1.9 3.46 0 0 0 0 0 1 0 0 0 0
91 1.6 3.57 0 0 0 0 0 0 1 0 0 0
92 1.6 3.60 0 0 0 0 0 0 0 1 0 0
93 1.2 3.51 0 0 0 0 0 0 0 0 1 0
94 1.2 3.52 0 0 0 0 0 0 0 0 0 1
95 1.5 3.49 0 0 0 0 0 0 0 0 0 0
96 1.6 3.50 0 0 0 0 0 0 0 0 0 0
97 1.7 3.64 1 0 0 0 0 0 0 0 0 0
98 1.8 3.94 0 1 0 0 0 0 0 0 0 0
99 1.8 3.94 0 0 1 0 0 0 0 0 0 0
100 1.8 3.91 0 0 0 1 0 0 0 0 0 0
101 1.3 3.88 0 0 0 0 1 0 0 0 0 0
102 1.3 4.21 0 0 0 0 0 1 0 0 0 0
103 1.4 4.39 0 0 0 0 0 0 1 0 0 0
104 1.1 4.33 0 0 0 0 0 0 0 1 0 0
105 1.5 4.27 0 0 0 0 0 0 0 0 1 0
106 2.2 4.29 0 0 0 0 0 0 0 0 0 1
107 2.9 4.18 0 0 0 0 0 0 0 0 0 0
108 3.1 4.14 0 0 0 0 0 0 0 0 0 0
109 3.5 4.23 1 0 0 0 0 0 0 0 0 0
110 3.6 4.07 0 1 0 0 0 0 0 0 0 0
111 4.4 3.74 0 0 1 0 0 0 0 0 0 0
112 4.2 3.66 0 0 0 1 0 0 0 0 0 0
113 5.2 3.92 0 0 0 0 1 0 0 0 0 0
114 5.8 4.45 0 0 0 0 0 1 0 0 0 0
115 5.9 4.92 0 0 0 0 0 0 1 0 0 0
116 5.4 4.90 0 0 0 0 0 0 0 1 0 0
117 5.5 4.54 0 0 0 0 0 0 0 0 1 0
118 4.7 4.53 0 0 0 0 0 0 0 0 0 1
119 3.1 4.14 0 0 0 0 0 0 0 0 0 0
120 2.6 4.05 0 0 0 0 0 0 0 0 0 0
121 2.3 3.92 1 0 0 0 0 0 0 0 0 0
122 1.9 3.68 0 1 0 0 0 0 0 0 0 0
123 0.6 3.35 0 0 1 0 0 0 0 0 0 0
124 0.6 3.38 0 0 0 1 0 0 0 0 0 0
125 -0.4 3.44 0 0 0 0 1 0 0 0 0 0
126 -1.1 3.50 0 0 0 0 0 1 0 0 0 0
127 -1.7 3.54 0 0 0 0 0 0 1 0 0 0
128 -0.8 3.52 0 0 0 0 0 0 0 1 0 0
129 -1.2 3.53 0 0 0 0 0 0 0 0 1 0
130 -1.0 3.55 0 0 0 0 0 0 0 0 0 1
131 -0.1 3.37 0 0 0 0 0 0 0 0 0 0
132 0.3 3.36 0 0 0 0 0 0 0 0 0 0
M11 t
1 0 1
2 0 2
3 0 3
4 0 4
5 0 5
6 0 6
7 0 7
8 0 8
9 0 9
10 0 10
11 1 11
12 0 12
13 0 13
14 0 14
15 0 15
16 0 16
17 0 17
18 0 18
19 0 19
20 0 20
21 0 21
22 0 22
23 1 23
24 0 24
25 0 25
26 0 26
27 0 27
28 0 28
29 0 29
30 0 30
31 0 31
32 0 32
33 0 33
34 0 34
35 1 35
36 0 36
37 0 37
38 0 38
39 0 39
40 0 40
41 0 41
42 0 42
43 0 43
44 0 44
45 0 45
46 0 46
47 1 47
48 0 48
49 0 49
50 0 50
51 0 51
52 0 52
53 0 53
54 0 54
55 0 55
56 0 56
57 0 57
58 0 58
59 1 59
60 0 60
61 0 61
62 0 62
63 0 63
64 0 64
65 0 65
66 0 66
67 0 67
68 0 68
69 0 69
70 0 70
71 1 71
72 0 72
73 0 73
74 0 74
75 0 75
76 0 76
77 0 77
78 0 78
79 0 79
80 0 80
81 0 81
82 0 82
83 1 83
84 0 84
85 0 85
86 0 86
87 0 87
88 0 88
89 0 89
90 0 90
91 0 91
92 0 92
93 0 93
94 0 94
95 1 95
96 0 96
97 0 97
98 0 98
99 0 99
100 0 100
101 0 101
102 0 102
103 0 103
104 0 104
105 0 105
106 0 106
107 1 107
108 0 108
109 0 109
110 0 110
111 0 111
112 0 112
113 0 113
114 0 114
115 0 115
116 0 116
117 0 117
118 0 118
119 1 119
120 0 120
121 0 121
122 0 122
123 0 123
124 0 124
125 0 125
126 0 126
127 0 127
128 0 128
129 0 129
130 0 130
131 1 131
132 0 132
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-1.599e+03 1.011e-01 3.699e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1.447e-02 -1.301e+01 1.406e+01
Alg_consumptie_index_BE Gem_rente_kasbon_5j M1
3.373e+01 -2.698e+02 9.950e+01
M2 M3 M4
1.118e+02 6.477e+01 2.372e+01
M5 M6 M7
8.235e+00 -1.266e+01 3.622e+01
M8 M9 M10
4.651e+01 8.413e+01 1.499e+02
M11 t
8.604e+01 1.143e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-557.678 -132.046 -2.761 171.595 676.117
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.599e+03 3.654e+02 -4.376 2.73e-05 ***
Nikkei 1.011e-01 1.851e-02 5.460 2.90e-07 ***
DJ_Indust 3.699e-01 4.063e-02 9.104 3.84e-15 ***
Goudprijs 1.447e-02 2.099e-02 0.690 0.4919
Conjunct_Seizoenzuiver -1.301e+01 7.723e+00 -1.684 0.0949 .
Cons_vertrouw 1.406e+01 7.315e+00 1.922 0.0572 .
Alg_consumptie_index_BE 3.373e+01 2.464e+01 1.369 0.1738
Gem_rente_kasbon_5j -2.698e+02 6.323e+01 -4.266 4.17e-05 ***
M1 9.950e+01 1.174e+02 0.848 0.3984
M2 1.118e+02 1.183e+02 0.945 0.3467
M3 6.477e+01 1.180e+02 0.549 0.5840
M4 2.372e+01 1.183e+02 0.201 0.8414
M5 8.235e+00 1.158e+02 0.071 0.9435
M6 -1.266e+01 1.158e+02 -0.109 0.9131
M7 3.622e+01 1.165e+02 0.311 0.7564
M8 4.651e+01 1.167e+02 0.398 0.6911
M9 8.413e+01 1.161e+02 0.725 0.4700
M10 1.499e+02 1.161e+02 1.292 0.1992
M11 8.604e+01 1.150e+02 0.748 0.4558
t 1.143e+00 2.740e+00 0.417 0.6772
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 269.1 on 112 degrees of freedom
Multiple R-squared: 0.8908, Adjusted R-squared: 0.8723
F-statistic: 48.08 on 19 and 112 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,] 1.432478e-01 2.864956e-01 8.567522e-01
[2,] 7.321870e-02 1.464374e-01 9.267813e-01
[3,] 4.199924e-02 8.399847e-02 9.580008e-01
[4,] 1.919040e-02 3.838080e-02 9.808096e-01
[5,] 7.864265e-03 1.572853e-02 9.921357e-01
[6,] 3.086912e-03 6.173824e-03 9.969131e-01
[7,] 1.676449e-03 3.352898e-03 9.983236e-01
[8,] 6.863189e-04 1.372638e-03 9.993137e-01
[9,] 2.837450e-04 5.674900e-04 9.997163e-01
[10,] 9.169802e-05 1.833960e-04 9.999083e-01
[11,] 8.158181e-05 1.631636e-04 9.999184e-01
[12,] 2.945204e-05 5.890408e-05 9.999705e-01
[13,] 1.320531e-05 2.641062e-05 9.999868e-01
[14,] 4.989166e-06 9.978333e-06 9.999950e-01
[15,] 1.689307e-06 3.378615e-06 9.999983e-01
[16,] 1.701866e-06 3.403733e-06 9.999983e-01
[17,] 1.719654e-06 3.439307e-06 9.999983e-01
[18,] 1.699023e-05 3.398045e-05 9.999830e-01
[19,] 1.910042e-04 3.820084e-04 9.998090e-01
[20,] 1.065934e-04 2.131868e-04 9.998934e-01
[21,] 6.664123e-05 1.332825e-04 9.999334e-01
[22,] 7.185586e-05 1.437117e-04 9.999281e-01
[23,] 7.801553e-05 1.560311e-04 9.999220e-01
[24,] 1.497381e-04 2.994763e-04 9.998503e-01
[25,] 7.517617e-04 1.503523e-03 9.992482e-01
[26,] 1.861487e-03 3.722973e-03 9.981385e-01
[27,] 2.726562e-03 5.453125e-03 9.972734e-01
[28,] 2.217877e-03 4.435755e-03 9.977821e-01
[29,] 1.379936e-03 2.759871e-03 9.986201e-01
[30,] 2.307089e-03 4.614178e-03 9.976929e-01
[31,] 4.904707e-03 9.809413e-03 9.950953e-01
[32,] 5.918214e-03 1.183643e-02 9.940818e-01
[33,] 6.712146e-03 1.342429e-02 9.932879e-01
[34,] 1.110485e-02 2.220970e-02 9.888951e-01
[35,] 8.993373e-03 1.798675e-02 9.910066e-01
[36,] 2.251533e-02 4.503066e-02 9.774847e-01
[37,] 1.991425e-02 3.982850e-02 9.800857e-01
[38,] 1.725187e-02 3.450374e-02 9.827481e-01
[39,] 1.788326e-02 3.576652e-02 9.821167e-01
[40,] 1.965109e-02 3.930218e-02 9.803489e-01
[41,] 5.982269e-02 1.196454e-01 9.401773e-01
[42,] 2.943892e-01 5.887785e-01 7.056108e-01
[43,] 7.171302e-01 5.657396e-01 2.828698e-01
[44,] 8.785494e-01 2.429013e-01 1.214506e-01
[45,] 9.509390e-01 9.812192e-02 4.906096e-02
[46,] 9.743484e-01 5.130326e-02 2.565163e-02
[47,] 9.888117e-01 2.237651e-02 1.118826e-02
[48,] 9.964386e-01 7.122714e-03 3.561357e-03
[49,] 9.988195e-01 2.360902e-03 1.180451e-03
[50,] 9.997360e-01 5.279793e-04 2.639896e-04
[51,] 9.999552e-01 8.950116e-05 4.475058e-05
[52,] 9.999918e-01 1.635604e-05 8.178020e-06
[53,] 9.999994e-01 1.104102e-06 5.520509e-07
[54,] 9.999999e-01 2.680024e-07 1.340012e-07
[55,] 1.000000e+00 4.038817e-08 2.019408e-08
[56,] 1.000000e+00 2.666548e-08 1.333274e-08
[57,] 1.000000e+00 4.426876e-08 2.213438e-08
[58,] 1.000000e+00 4.557168e-08 2.278584e-08
[59,] 1.000000e+00 8.120665e-08 4.060332e-08
[60,] 9.999999e-01 1.754702e-07 8.773511e-08
[61,] 9.999999e-01 2.400115e-07 1.200058e-07
[62,] 9.999998e-01 3.938936e-07 1.969468e-07
[63,] 9.999998e-01 3.826585e-07 1.913293e-07
[64,] 9.999999e-01 2.493921e-07 1.246960e-07
[65,] 1.000000e+00 8.885610e-08 4.442805e-08
[66,] 1.000000e+00 1.471774e-08 7.358868e-09
[67,] 1.000000e+00 3.278515e-09 1.639258e-09
[68,] 1.000000e+00 4.030983e-10 2.015491e-10
[69,] 1.000000e+00 1.139834e-09 5.699169e-10
[70,] 1.000000e+00 3.431996e-09 1.715998e-09
[71,] 1.000000e+00 1.172082e-08 5.860412e-09
[72,] 1.000000e+00 1.292569e-08 6.462847e-09
[73,] 1.000000e+00 3.590163e-08 1.795081e-08
[74,] 9.999999e-01 1.294562e-07 6.472811e-08
[75,] 9.999999e-01 2.944241e-07 1.472121e-07
[76,] 9.999997e-01 5.020271e-07 2.510136e-07
[77,] 9.999994e-01 1.235797e-06 6.178986e-07
[78,] 9.999976e-01 4.882338e-06 2.441169e-06
[79,] 9.999907e-01 1.869126e-05 9.345630e-06
[80,] 9.999765e-01 4.709928e-05 2.354964e-05
[81,] 9.999455e-01 1.090007e-04 5.450034e-05
[82,] 9.997802e-01 4.396920e-04 2.198460e-04
[83,] 9.993670e-01 1.266005e-03 6.330023e-04
[84,] 9.988926e-01 2.214882e-03 1.107441e-03
[85,] 9.985251e-01 2.949804e-03 1.474902e-03
[86,] 9.937531e-01 1.249383e-02 6.246914e-03
[87,] 9.738080e-01 5.238406e-02 2.619203e-02
> postscript(file="/var/www/html/rcomp/tmp/11v4w1291653624.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/21v4w1291653624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/html/rcomp/tmp/3cmmh1291653624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4cmmh1291653624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5cmmh1291653624.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 = 132
Frequency = 1
1 2 3 4 5 6
676.117443 594.985396 276.492495 -66.755700 -262.393469 2.171159
7 8 9 10 11 12
-312.183345 -212.083478 -38.989197 -83.100155 -134.612258 -153.598864
13 14 15 16 17 18
-508.987600 -521.771143 -523.786085 -557.677759 -123.832673 -95.674302
19 20 21 22 23 24
-60.106350 -32.063350 -75.057897 90.334476 140.749198 43.386717
25 26 27 28 29 30
-42.871203 7.204356 169.898357 53.351822 -145.587241 -93.005666
31 32 33 34 35 36
125.077956 242.201617 479.458670 382.097847 267.918340 243.711134
37 38 39 40 41 42
176.683295 257.743521 28.433518 341.411403 347.710091 398.318281
43 44 45 46 47 48
420.662113 285.936707 268.982925 159.504555 145.880999 250.431155
49 50 51 52 53 54
30.284302 17.030876 -39.635191 56.452127 -108.524543 -406.620667
55 56 57 58 59 60
-403.234464 -280.515553 -489.738899 -391.176124 -352.955970 -371.312981
61 62 63 64 65 66
-535.802021 -466.262474 -424.392513 -442.961101 -221.720472 -380.366327
67 68 69 70 71 72
-204.160857 -168.741120 -119.041376 42.793919 34.283925 46.410176
73 74 75 76 77 78
17.057686 -103.421786 -85.899149 166.917163 191.356277 128.213444
79 80 81 82 83 84
49.065335 94.293631 92.490167 36.298852 -13.223309 -7.692240
85 86 87 88 89 90
-27.579847 66.259569 242.302239 189.491340 214.292922 255.938596
91 92 93 94 95 96
352.350892 350.972718 289.340768 140.138841 194.444087 377.595793
97 98 99 100 101 102
249.893163 226.756296 343.032843 377.337789 265.806029 272.197570
103 104 105 106 107 108
152.999435 72.686689 -11.434018 -206.292630 -40.234525 -123.397406
109 110 111 112 113 114
45.582406 -131.191182 -64.455465 -76.038243 -142.526561 -20.766608
115 116 117 118 119 120
-64.304686 -114.466795 -198.398385 31.922823 101.724925 -12.426250
121 122 123 124 125 126
-80.377624 52.666572 78.008950 -41.528840 -14.580360 -60.405479
127 128 129 130 131 132
-56.166031 -238.221067 -197.612759 -202.522405 -343.975413 -293.107235
> postscript(file="/var/www/html/rcomp/tmp/65vlk1291653624.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 = 132
Frequency = 1
lag(myerror, k = 1) myerror
0 676.117443 NA
1 594.985396 676.117443
2 276.492495 594.985396
3 -66.755700 276.492495
4 -262.393469 -66.755700
5 2.171159 -262.393469
6 -312.183345 2.171159
7 -212.083478 -312.183345
8 -38.989197 -212.083478
9 -83.100155 -38.989197
10 -134.612258 -83.100155
11 -153.598864 -134.612258
12 -508.987600 -153.598864
13 -521.771143 -508.987600
14 -523.786085 -521.771143
15 -557.677759 -523.786085
16 -123.832673 -557.677759
17 -95.674302 -123.832673
18 -60.106350 -95.674302
19 -32.063350 -60.106350
20 -75.057897 -32.063350
21 90.334476 -75.057897
22 140.749198 90.334476
23 43.386717 140.749198
24 -42.871203 43.386717
25 7.204356 -42.871203
26 169.898357 7.204356
27 53.351822 169.898357
28 -145.587241 53.351822
29 -93.005666 -145.587241
30 125.077956 -93.005666
31 242.201617 125.077956
32 479.458670 242.201617
33 382.097847 479.458670
34 267.918340 382.097847
35 243.711134 267.918340
36 176.683295 243.711134
37 257.743521 176.683295
38 28.433518 257.743521
39 341.411403 28.433518
40 347.710091 341.411403
41 398.318281 347.710091
42 420.662113 398.318281
43 285.936707 420.662113
44 268.982925 285.936707
45 159.504555 268.982925
46 145.880999 159.504555
47 250.431155 145.880999
48 30.284302 250.431155
49 17.030876 30.284302
50 -39.635191 17.030876
51 56.452127 -39.635191
52 -108.524543 56.452127
53 -406.620667 -108.524543
54 -403.234464 -406.620667
55 -280.515553 -403.234464
56 -489.738899 -280.515553
57 -391.176124 -489.738899
58 -352.955970 -391.176124
59 -371.312981 -352.955970
60 -535.802021 -371.312981
61 -466.262474 -535.802021
62 -424.392513 -466.262474
63 -442.961101 -424.392513
64 -221.720472 -442.961101
65 -380.366327 -221.720472
66 -204.160857 -380.366327
67 -168.741120 -204.160857
68 -119.041376 -168.741120
69 42.793919 -119.041376
70 34.283925 42.793919
71 46.410176 34.283925
72 17.057686 46.410176
73 -103.421786 17.057686
74 -85.899149 -103.421786
75 166.917163 -85.899149
76 191.356277 166.917163
77 128.213444 191.356277
78 49.065335 128.213444
79 94.293631 49.065335
80 92.490167 94.293631
81 36.298852 92.490167
82 -13.223309 36.298852
83 -7.692240 -13.223309
84 -27.579847 -7.692240
85 66.259569 -27.579847
86 242.302239 66.259569
87 189.491340 242.302239
88 214.292922 189.491340
89 255.938596 214.292922
90 352.350892 255.938596
91 350.972718 352.350892
92 289.340768 350.972718
93 140.138841 289.340768
94 194.444087 140.138841
95 377.595793 194.444087
96 249.893163 377.595793
97 226.756296 249.893163
98 343.032843 226.756296
99 377.337789 343.032843
100 265.806029 377.337789
101 272.197570 265.806029
102 152.999435 272.197570
103 72.686689 152.999435
104 -11.434018 72.686689
105 -206.292630 -11.434018
106 -40.234525 -206.292630
107 -123.397406 -40.234525
108 45.582406 -123.397406
109 -131.191182 45.582406
110 -64.455465 -131.191182
111 -76.038243 -64.455465
112 -142.526561 -76.038243
113 -20.766608 -142.526561
114 -64.304686 -20.766608
115 -114.466795 -64.304686
116 -198.398385 -114.466795
117 31.922823 -198.398385
118 101.724925 31.922823
119 -12.426250 101.724925
120 -80.377624 -12.426250
121 52.666572 -80.377624
122 78.008950 52.666572
123 -41.528840 78.008950
124 -14.580360 -41.528840
125 -60.405479 -14.580360
126 -56.166031 -60.405479
127 -238.221067 -56.166031
128 -197.612759 -238.221067
129 -202.522405 -197.612759
130 -343.975413 -202.522405
131 -293.107235 -343.975413
132 NA -293.107235
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 594.985396 676.117443
[2,] 276.492495 594.985396
[3,] -66.755700 276.492495
[4,] -262.393469 -66.755700
[5,] 2.171159 -262.393469
[6,] -312.183345 2.171159
[7,] -212.083478 -312.183345
[8,] -38.989197 -212.083478
[9,] -83.100155 -38.989197
[10,] -134.612258 -83.100155
[11,] -153.598864 -134.612258
[12,] -508.987600 -153.598864
[13,] -521.771143 -508.987600
[14,] -523.786085 -521.771143
[15,] -557.677759 -523.786085
[16,] -123.832673 -557.677759
[17,] -95.674302 -123.832673
[18,] -60.106350 -95.674302
[19,] -32.063350 -60.106350
[20,] -75.057897 -32.063350
[21,] 90.334476 -75.057897
[22,] 140.749198 90.334476
[23,] 43.386717 140.749198
[24,] -42.871203 43.386717
[25,] 7.204356 -42.871203
[26,] 169.898357 7.204356
[27,] 53.351822 169.898357
[28,] -145.587241 53.351822
[29,] -93.005666 -145.587241
[30,] 125.077956 -93.005666
[31,] 242.201617 125.077956
[32,] 479.458670 242.201617
[33,] 382.097847 479.458670
[34,] 267.918340 382.097847
[35,] 243.711134 267.918340
[36,] 176.683295 243.711134
[37,] 257.743521 176.683295
[38,] 28.433518 257.743521
[39,] 341.411403 28.433518
[40,] 347.710091 341.411403
[41,] 398.318281 347.710091
[42,] 420.662113 398.318281
[43,] 285.936707 420.662113
[44,] 268.982925 285.936707
[45,] 159.504555 268.982925
[46,] 145.880999 159.504555
[47,] 250.431155 145.880999
[48,] 30.284302 250.431155
[49,] 17.030876 30.284302
[50,] -39.635191 17.030876
[51,] 56.452127 -39.635191
[52,] -108.524543 56.452127
[53,] -406.620667 -108.524543
[54,] -403.234464 -406.620667
[55,] -280.515553 -403.234464
[56,] -489.738899 -280.515553
[57,] -391.176124 -489.738899
[58,] -352.955970 -391.176124
[59,] -371.312981 -352.955970
[60,] -535.802021 -371.312981
[61,] -466.262474 -535.802021
[62,] -424.392513 -466.262474
[63,] -442.961101 -424.392513
[64,] -221.720472 -442.961101
[65,] -380.366327 -221.720472
[66,] -204.160857 -380.366327
[67,] -168.741120 -204.160857
[68,] -119.041376 -168.741120
[69,] 42.793919 -119.041376
[70,] 34.283925 42.793919
[71,] 46.410176 34.283925
[72,] 17.057686 46.410176
[73,] -103.421786 17.057686
[74,] -85.899149 -103.421786
[75,] 166.917163 -85.899149
[76,] 191.356277 166.917163
[77,] 128.213444 191.356277
[78,] 49.065335 128.213444
[79,] 94.293631 49.065335
[80,] 92.490167 94.293631
[81,] 36.298852 92.490167
[82,] -13.223309 36.298852
[83,] -7.692240 -13.223309
[84,] -27.579847 -7.692240
[85,] 66.259569 -27.579847
[86,] 242.302239 66.259569
[87,] 189.491340 242.302239
[88,] 214.292922 189.491340
[89,] 255.938596 214.292922
[90,] 352.350892 255.938596
[91,] 350.972718 352.350892
[92,] 289.340768 350.972718
[93,] 140.138841 289.340768
[94,] 194.444087 140.138841
[95,] 377.595793 194.444087
[96,] 249.893163 377.595793
[97,] 226.756296 249.893163
[98,] 343.032843 226.756296
[99,] 377.337789 343.032843
[100,] 265.806029 377.337789
[101,] 272.197570 265.806029
[102,] 152.999435 272.197570
[103,] 72.686689 152.999435
[104,] -11.434018 72.686689
[105,] -206.292630 -11.434018
[106,] -40.234525 -206.292630
[107,] -123.397406 -40.234525
[108,] 45.582406 -123.397406
[109,] -131.191182 45.582406
[110,] -64.455465 -131.191182
[111,] -76.038243 -64.455465
[112,] -142.526561 -76.038243
[113,] -20.766608 -142.526561
[114,] -64.304686 -20.766608
[115,] -114.466795 -64.304686
[116,] -198.398385 -114.466795
[117,] 31.922823 -198.398385
[118,] 101.724925 31.922823
[119,] -12.426250 101.724925
[120,] -80.377624 -12.426250
[121,] 52.666572 -80.377624
[122,] 78.008950 52.666572
[123,] -41.528840 78.008950
[124,] -14.580360 -41.528840
[125,] -60.405479 -14.580360
[126,] -56.166031 -60.405479
[127,] -238.221067 -56.166031
[128,] -197.612759 -238.221067
[129,] -202.522405 -197.612759
[130,] -343.975413 -202.522405
[131,] -293.107235 -343.975413
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 594.985396 676.117443
2 276.492495 594.985396
3 -66.755700 276.492495
4 -262.393469 -66.755700
5 2.171159 -262.393469
6 -312.183345 2.171159
7 -212.083478 -312.183345
8 -38.989197 -212.083478
9 -83.100155 -38.989197
10 -134.612258 -83.100155
11 -153.598864 -134.612258
12 -508.987600 -153.598864
13 -521.771143 -508.987600
14 -523.786085 -521.771143
15 -557.677759 -523.786085
16 -123.832673 -557.677759
17 -95.674302 -123.832673
18 -60.106350 -95.674302
19 -32.063350 -60.106350
20 -75.057897 -32.063350
21 90.334476 -75.057897
22 140.749198 90.334476
23 43.386717 140.749198
24 -42.871203 43.386717
25 7.204356 -42.871203
26 169.898357 7.204356
27 53.351822 169.898357
28 -145.587241 53.351822
29 -93.005666 -145.587241
30 125.077956 -93.005666
31 242.201617 125.077956
32 479.458670 242.201617
33 382.097847 479.458670
34 267.918340 382.097847
35 243.711134 267.918340
36 176.683295 243.711134
37 257.743521 176.683295
38 28.433518 257.743521
39 341.411403 28.433518
40 347.710091 341.411403
41 398.318281 347.710091
42 420.662113 398.318281
43 285.936707 420.662113
44 268.982925 285.936707
45 159.504555 268.982925
46 145.880999 159.504555
47 250.431155 145.880999
48 30.284302 250.431155
49 17.030876 30.284302
50 -39.635191 17.030876
51 56.452127 -39.635191
52 -108.524543 56.452127
53 -406.620667 -108.524543
54 -403.234464 -406.620667
55 -280.515553 -403.234464
56 -489.738899 -280.515553
57 -391.176124 -489.738899
58 -352.955970 -391.176124
59 -371.312981 -352.955970
60 -535.802021 -371.312981
61 -466.262474 -535.802021
62 -424.392513 -466.262474
63 -442.961101 -424.392513
64 -221.720472 -442.961101
65 -380.366327 -221.720472
66 -204.160857 -380.366327
67 -168.741120 -204.160857
68 -119.041376 -168.741120
69 42.793919 -119.041376
70 34.283925 42.793919
71 46.410176 34.283925
72 17.057686 46.410176
73 -103.421786 17.057686
74 -85.899149 -103.421786
75 166.917163 -85.899149
76 191.356277 166.917163
77 128.213444 191.356277
78 49.065335 128.213444
79 94.293631 49.065335
80 92.490167 94.293631
81 36.298852 92.490167
82 -13.223309 36.298852
83 -7.692240 -13.223309
84 -27.579847 -7.692240
85 66.259569 -27.579847
86 242.302239 66.259569
87 189.491340 242.302239
88 214.292922 189.491340
89 255.938596 214.292922
90 352.350892 255.938596
91 350.972718 352.350892
92 289.340768 350.972718
93 140.138841 289.340768
94 194.444087 140.138841
95 377.595793 194.444087
96 249.893163 377.595793
97 226.756296 249.893163
98 343.032843 226.756296
99 377.337789 343.032843
100 265.806029 377.337789
101 272.197570 265.806029
102 152.999435 272.197570
103 72.686689 152.999435
104 -11.434018 72.686689
105 -206.292630 -11.434018
106 -40.234525 -206.292630
107 -123.397406 -40.234525
108 45.582406 -123.397406
109 -131.191182 45.582406
110 -64.455465 -131.191182
111 -76.038243 -64.455465
112 -142.526561 -76.038243
113 -20.766608 -142.526561
114 -64.304686 -20.766608
115 -114.466795 -64.304686
116 -198.398385 -114.466795
117 31.922823 -198.398385
118 101.724925 31.922823
119 -12.426250 101.724925
120 -80.377624 -12.426250
121 52.666572 -80.377624
122 78.008950 52.666572
123 -41.528840 78.008950
124 -14.580360 -41.528840
125 -60.405479 -14.580360
126 -56.166031 -60.405479
127 -238.221067 -56.166031
128 -197.612759 -238.221067
129 -202.522405 -197.612759
130 -343.975413 -202.522405
131 -293.107235 -343.975413
> 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/html/rcomp/tmp/7xnkn1291653624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8xnkn1291653624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9xnkn1291653624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/108ej81291653624.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/html/rcomp/tmp/11teie1291653624.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/html/rcomp/tmp/12ffg11291653624.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/html/rcomp/tmp/13t7es1291653624.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/html/rcomp/tmp/14e7vy1291653624.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/html/rcomp/tmp/15i8um1291653624.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/html/rcomp/tmp/16lras1291653624.tab")
+ }
>
> try(system("convert tmp/11v4w1291653624.ps tmp/11v4w1291653624.png",intern=TRUE))
character(0)
> try(system("convert tmp/21v4w1291653624.ps tmp/21v4w1291653624.png",intern=TRUE))
character(0)
> try(system("convert tmp/3cmmh1291653624.ps tmp/3cmmh1291653624.png",intern=TRUE))
character(0)
> try(system("convert tmp/4cmmh1291653624.ps tmp/4cmmh1291653624.png",intern=TRUE))
character(0)
> try(system("convert tmp/5cmmh1291653624.ps tmp/5cmmh1291653624.png",intern=TRUE))
character(0)
> try(system("convert tmp/65vlk1291653624.ps tmp/65vlk1291653624.png",intern=TRUE))
character(0)
> try(system("convert tmp/7xnkn1291653624.ps tmp/7xnkn1291653624.png",intern=TRUE))
character(0)
> try(system("convert tmp/8xnkn1291653624.ps tmp/8xnkn1291653624.png",intern=TRUE))
character(0)
> try(system("convert tmp/9xnkn1291653624.ps tmp/9xnkn1291653624.png",intern=TRUE))
character(0)
> try(system("convert tmp/108ej81291653624.ps tmp/108ej81291653624.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.896 1.806 14.007