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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+ ,1.85
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+ ,10302.87
+ ,9634.97
+ ,22052
+ ,-17.8
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+ ,10066.24
+ ,9857.34
+ ,22680
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+ ,10433.44
+ ,24977
+ ,-7.9
+ ,-15
+ ,0.3
+ ,1.56)
+ ,dim=c(8
+ ,132)
+ ,dimnames=list(c('BEL_20'
+ ,'Nikkei'
+ ,'DJ_Indust'
+ ,'Goudprijs'
+ ,'Conjunct_Seizoenzuiver'
+ ,'Cons_vertrouw'
+ ,'Alg_consumptie_index_BE'
+ ,'Gem_rente_kasbon_1j')
+ ,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_1j'),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_1j M1 M2 M3 M4 M5 M6 M7 M8 M9 M10
1 1.0 2.77 1 0 0 0 0 0 0 0 0 0
2 1.0 2.76 0 1 0 0 0 0 0 0 0 0
3 1.2 2.76 0 0 1 0 0 0 0 0 0 0
4 1.2 2.46 0 0 0 1 0 0 0 0 0 0
5 0.8 2.46 0 0 0 0 1 0 0 0 0 0
6 0.7 2.47 0 0 0 0 0 1 0 0 0 0
7 0.7 2.71 0 0 0 0 0 0 1 0 0 0
8 0.9 2.80 0 0 0 0 0 0 0 1 0 0
9 1.2 2.89 0 0 0 0 0 0 0 0 1 0
10 1.3 3.36 0 0 0 0 0 0 0 0 0 1
11 1.5 3.31 0 0 0 0 0 0 0 0 0 0
12 1.9 3.50 0 0 0 0 0 0 0 0 0 0
13 1.8 3.51 1 0 0 0 0 0 0 0 0 0
14 1.9 3.71 0 1 0 0 0 0 0 0 0 0
15 2.2 3.71 0 0 1 0 0 0 0 0 0 0
16 2.1 3.71 0 0 0 1 0 0 0 0 0 0
17 2.2 4.21 0 0 0 0 1 0 0 0 0 0
18 2.7 4.21 0 0 0 0 0 1 0 0 0 0
19 2.8 4.21 0 0 0 0 0 0 1 0 0 0
20 2.9 4.50 0 0 0 0 0 0 0 1 0 0
21 3.4 4.51 0 0 0 0 0 0 0 0 1 0
22 3.0 4.51 0 0 0 0 0 0 0 0 0 1
23 3.1 4.51 0 0 0 0 0 0 0 0 0 0
24 2.5 4.32 0 0 0 0 0 0 0 0 0 0
25 2.2 4.02 1 0 0 0 0 0 0 0 0 0
26 2.3 4.02 0 1 0 0 0 0 0 0 0 0
27 2.1 3.85 0 0 1 0 0 0 0 0 0 0
28 2.8 3.84 0 0 0 1 0 0 0 0 0 0
29 3.1 4.02 0 0 0 0 1 0 0 0 0 0
30 2.9 3.82 0 0 0 0 0 1 0 0 0 0
31 2.6 3.75 0 0 0 0 0 0 1 0 0 0
32 2.7 3.74 0 0 0 0 0 0 0 1 0 0
33 2.3 3.14 0 0 0 0 0 0 0 0 1 0
34 2.3 2.91 0 0 0 0 0 0 0 0 0 1
35 2.1 2.84 0 0 0 0 0 0 0 0 0 0
36 2.2 2.85 0 0 0 0 0 0 0 0 0 0
37 2.9 2.85 1 0 0 0 0 0 0 0 0 0
38 2.6 3.08 0 1 0 0 0 0 0 0 0 0
39 2.7 3.30 0 0 1 0 0 0 0 0 0 0
40 1.8 3.29 0 0 0 1 0 0 0 0 0 0
41 1.3 3.26 0 0 0 0 1 0 0 0 0 0
42 0.9 3.26 0 0 0 0 0 1 0 0 0 0
43 1.3 3.11 0 0 0 0 0 0 1 0 0 0
44 1.3 2.84 0 0 0 0 0 0 0 1 0 0
45 1.3 2.71 0 0 0 0 0 0 0 0 1 0
46 1.3 2.69 0 0 0 0 0 0 0 0 0 1
47 1.1 2.65 0 0 0 0 0 0 0 0 0 0
48 1.4 2.57 0 0 0 0 0 0 0 0 0 0
49 1.2 2.32 1 0 0 0 0 0 0 0 0 0
50 1.7 2.12 0 1 0 0 0 0 0 0 0 0
51 1.8 2.05 0 0 1 0 0 0 0 0 0 0
52 1.5 2.05 0 0 0 1 0 0 0 0 0 0
53 1.0 1.81 0 0 0 0 1 0 0 0 0 0
54 1.6 1.58 0 0 0 0 0 1 0 0 0 0
55 1.5 1.57 0 0 0 0 0 0 1 0 0 0
56 1.8 1.76 0 0 0 0 0 0 0 1 0 0
57 1.8 1.76 0 0 0 0 0 0 0 0 1 0
58 1.6 1.89 0 0 0 0 0 0 0 0 0 1
59 1.9 1.90 0 0 0 0 0 0 0 0 0 0
60 1.7 1.90 0 0 0 0 0 0 0 0 0 0
61 1.6 1.92 1 0 0 0 0 0 0 0 0 0
62 1.3 1.76 0 1 0 0 0 0 0 0 0 0
63 1.1 1.64 0 0 1 0 0 0 0 0 0 0
64 1.9 1.57 0 0 0 1 0 0 0 0 0 0
65 2.6 1.69 0 0 0 0 1 0 0 0 0 0
66 2.3 1.76 0 0 0 0 0 1 0 0 0 0
67 2.4 1.89 0 0 0 0 0 0 1 0 0 0
68 2.2 1.78 0 0 0 0 0 0 0 1 0 0
69 2.0 1.88 0 0 0 0 0 0 0 0 1 0
70 2.9 1.86 0 0 0 0 0 0 0 0 0 1
71 2.6 1.88 0 0 0 0 0 0 0 0 0 0
72 2.3 1.87 0 0 0 0 0 0 0 0 0 0
73 2.3 1.86 1 0 0 0 0 0 0 0 0 0
74 2.6 1.89 0 1 0 0 0 0 0 0 0 0
75 3.1 1.90 0 0 1 0 0 0 0 0 0 0
76 2.8 1.89 0 0 0 1 0 0 0 0 0 0
77 2.5 1.85 0 0 0 0 1 0 0 0 0 0
78 2.9 1.78 0 0 0 0 0 1 0 0 0 0
79 3.1 1.71 0 0 0 0 0 0 1 0 0 0
80 3.1 1.69 0 0 0 0 0 0 0 1 0 0
81 3.2 1.72 0 0 0 0 0 0 0 0 1 0
82 2.5 1.77 0 0 0 0 0 0 0 0 0 1
83 2.6 1.98 0 0 0 0 0 0 0 0 0 0
84 2.9 2.20 0 0 0 0 0 0 0 0 0 0
85 2.6 2.25 1 0 0 0 0 0 0 0 0 0
86 2.4 2.24 0 1 0 0 0 0 0 0 0 0
87 1.7 2.51 0 0 1 0 0 0 0 0 0 0
88 2.0 2.79 0 0 0 1 0 0 0 0 0 0
89 2.2 3.07 0 0 0 0 1 0 0 0 0 0
90 1.9 3.08 0 0 0 0 0 1 0 0 0 0
91 1.6 3.05 0 0 0 0 0 0 1 0 0 0
92 1.6 3.08 0 0 0 0 0 0 0 1 0 0
93 1.2 3.15 0 0 0 0 0 0 0 0 1 0
94 1.2 3.16 0 0 0 0 0 0 0 0 0 1
95 1.5 3.16 0 0 0 0 0 0 0 0 0 0
96 1.6 3.19 0 0 0 0 0 0 0 0 0 0
97 1.7 3.44 1 0 0 0 0 0 0 0 0 0
98 1.8 3.55 0 1 0 0 0 0 0 0 0 0
99 1.8 3.60 0 0 1 0 0 0 0 0 0 0
100 1.8 3.62 0 0 0 1 0 0 0 0 0 0
101 1.3 3.69 0 0 0 0 1 0 0 0 0 0
102 1.3 3.99 0 0 0 0 0 1 0 0 0 0
103 1.4 4.06 0 0 0 0 0 0 1 0 0 0
104 1.1 4.05 0 0 0 0 0 0 0 1 0 0
105 1.5 4.01 0 0 0 0 0 0 0 0 1 0
106 2.2 3.98 0 0 0 0 0 0 0 0 0 1
107 2.9 3.94 0 0 0 0 0 0 0 0 0 0
108 3.1 3.92 0 0 0 0 0 0 0 0 0 0
109 3.5 4.10 1 0 0 0 0 0 0 0 0 0
110 3.6 3.88 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 3.97 0 0 0 1 0 0 0 0 0 0
113 5.2 4.26 0 0 0 0 1 0 0 0 0 0
114 5.8 4.63 0 0 0 0 0 1 0 0 0 0
115 5.9 4.82 0 0 0 0 0 0 1 0 0 0
116 5.4 4.94 0 0 0 0 0 0 0 1 0 0
117 5.5 4.98 0 0 0 0 0 0 0 0 1 0
118 4.7 5.02 0 0 0 0 0 0 0 0 0 1
119 3.1 4.96 0 0 0 0 0 0 0 0 0 0
120 2.6 4.49 0 0 0 0 0 0 0 0 0 0
121 2.3 3.50 1 0 0 0 0 0 0 0 0 0
122 1.9 2.95 0 1 0 0 0 0 0 0 0 0
123 0.6 2.37 0 0 1 0 0 0 0 0 0 0
124 0.6 2.16 0 0 0 1 0 0 0 0 0 0
125 -0.4 2.08 0 0 0 0 1 0 0 0 0 0
126 -1.1 1.98 0 0 0 0 0 1 0 0 0 0
127 -1.7 1.98 0 0 0 0 0 0 1 0 0 0
128 -0.8 1.85 0 0 0 0 0 0 0 1 0 0
129 -1.2 1.82 0 0 0 0 0 0 0 0 1 0
130 -1.0 1.65 0 0 0 0 0 0 0 0 0 1
131 -0.1 1.59 0 0 0 0 0 0 0 0 0 0
132 0.3 1.56 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
-1.830e+03 9.325e-02 3.406e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
-4.613e-02 -3.132e+00 -3.402e+00
Alg_consumptie_index_BE Gem_rente_kasbon_1j M1
-4.060e+01 4.615e+01 1.662e+02
M2 M3 M4
2.114e+02 1.457e+02 1.044e+02
M5 M6 M7
5.557e+01 -4.549e+00 -9.280e+00
M8 M9 M10
-1.805e+00 6.509e+01 1.024e+02
M11 t
7.818e+01 7.052e+00
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-683.54 -154.86 -31.87 198.84 950.72
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.830e+03 3.967e+02 -4.613 1.06e-05 ***
Nikkei 9.325e-02 2.015e-02 4.628 1.00e-05 ***
DJ_Indust 3.406e-01 4.394e-02 7.753 4.47e-12 ***
Goudprijs -4.613e-02 2.118e-02 -2.178 0.0315 *
Conjunct_Seizoenzuiver -3.132e+00 8.454e+00 -0.370 0.7117
Cons_vertrouw -3.402e+00 7.502e+00 -0.453 0.6511
Alg_consumptie_index_BE -4.060e+01 3.102e+01 -1.309 0.1933
Gem_rente_kasbon_1j 4.615e+01 4.700e+01 0.982 0.3283
M1 1.662e+02 1.254e+02 1.326 0.1876
M2 2.114e+02 1.260e+02 1.678 0.0961 .
M3 1.457e+02 1.259e+02 1.157 0.2499
M4 1.044e+02 1.264e+02 0.826 0.4108
M5 5.557e+01 1.239e+02 0.448 0.6547
M6 -4.549e+00 1.243e+02 -0.037 0.9709
M7 -9.280e+00 1.247e+02 -0.074 0.9408
M8 -1.805e+00 1.249e+02 -0.014 0.9885
M9 6.509e+01 1.246e+02 0.523 0.6023
M10 1.024e+02 1.244e+02 0.824 0.4119
M11 7.818e+01 1.235e+02 0.633 0.5281
t 7.052e+00 2.796e+00 2.523 0.0131 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 288.9 on 112 degrees of freedom
Multiple R-squared: 0.8741, Adjusted R-squared: 0.8528
F-statistic: 40.93 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,] 1.712011e-01 3.424023e-01 8.287989e-01
[2,] 7.797231e-02 1.559446e-01 9.220277e-01
[3,] 4.448017e-02 8.896034e-02 9.555198e-01
[4,] 1.967824e-02 3.935647e-02 9.803218e-01
[5,] 8.032528e-03 1.606506e-02 9.919675e-01
[6,] 3.193441e-03 6.386882e-03 9.968066e-01
[7,] 1.800285e-03 3.600570e-03 9.981997e-01
[8,] 7.425835e-04 1.485167e-03 9.992574e-01
[9,] 2.847966e-04 5.695932e-04 9.997152e-01
[10,] 9.231697e-05 1.846339e-04 9.999077e-01
[11,] 8.740746e-05 1.748149e-04 9.999126e-01
[12,] 3.222564e-05 6.445128e-05 9.999678e-01
[13,] 1.478211e-05 2.956423e-05 9.999852e-01
[14,] 7.362433e-06 1.472487e-05 9.999926e-01
[15,] 2.643849e-06 5.287698e-06 9.999974e-01
[16,] 1.811693e-06 3.623387e-06 9.999982e-01
[17,] 1.464643e-06 2.929285e-06 9.999985e-01
[18,] 1.801462e-05 3.602924e-05 9.999820e-01
[19,] 1.470906e-04 2.941811e-04 9.998529e-01
[20,] 7.576087e-05 1.515217e-04 9.999242e-01
[21,] 3.998196e-05 7.996392e-05 9.999600e-01
[22,] 3.807259e-05 7.614517e-05 9.999619e-01
[23,] 4.447519e-05 8.895037e-05 9.999555e-01
[24,] 6.023849e-05 1.204770e-04 9.999398e-01
[25,] 1.354992e-04 2.709984e-04 9.998645e-01
[26,] 1.689067e-04 3.378133e-04 9.998311e-01
[27,] 3.060303e-04 6.120607e-04 9.996940e-01
[28,] 1.957839e-04 3.915678e-04 9.998042e-01
[29,] 1.011484e-04 2.022969e-04 9.998989e-01
[30,] 8.758258e-05 1.751652e-04 9.999124e-01
[31,] 1.898982e-04 3.797963e-04 9.998101e-01
[32,] 2.634258e-04 5.268517e-04 9.997366e-01
[33,] 3.179922e-04 6.359845e-04 9.996820e-01
[34,] 6.858340e-04 1.371668e-03 9.993142e-01
[35,] 5.486656e-04 1.097331e-03 9.994513e-01
[36,] 1.918580e-03 3.837160e-03 9.980814e-01
[37,] 1.627038e-03 3.254076e-03 9.983730e-01
[38,] 1.327861e-03 2.655723e-03 9.986721e-01
[39,] 1.487045e-03 2.974089e-03 9.985130e-01
[40,] 1.876217e-03 3.752433e-03 9.981238e-01
[41,] 1.177773e-02 2.355547e-02 9.882223e-01
[42,] 1.218880e-01 2.437759e-01 8.781120e-01
[43,] 4.248431e-01 8.496863e-01 5.751569e-01
[44,] 6.460893e-01 7.078214e-01 3.539107e-01
[45,] 8.230851e-01 3.538298e-01 1.769149e-01
[46,] 9.041064e-01 1.917873e-01 9.589363e-02
[47,] 9.735526e-01 5.289477e-02 2.644738e-02
[48,] 9.921459e-01 1.570813e-02 7.854063e-03
[49,] 9.982665e-01 3.466914e-03 1.733457e-03
[50,] 9.998048e-01 3.903810e-04 1.951905e-04
[51,] 9.999851e-01 2.975241e-05 1.487621e-05
[52,] 9.999985e-01 2.927140e-06 1.463570e-06
[53,] 9.999999e-01 1.140542e-07 5.702709e-08
[54,] 1.000000e+00 2.196362e-08 1.098181e-08
[55,] 1.000000e+00 3.570334e-09 1.785167e-09
[56,] 1.000000e+00 2.029303e-09 1.014651e-09
[57,] 1.000000e+00 2.486122e-09 1.243061e-09
[58,] 1.000000e+00 1.328428e-09 6.642140e-10
[59,] 1.000000e+00 1.193193e-09 5.965965e-10
[60,] 1.000000e+00 1.210868e-09 6.054340e-10
[61,] 1.000000e+00 2.750163e-09 1.375082e-09
[62,] 1.000000e+00 6.709337e-09 3.354668e-09
[63,] 1.000000e+00 1.193296e-08 5.966480e-09
[64,] 1.000000e+00 1.709764e-08 8.548820e-09
[65,] 1.000000e+00 1.057184e-08 5.285920e-09
[66,] 1.000000e+00 2.068947e-09 1.034474e-09
[67,] 1.000000e+00 2.423028e-10 1.211514e-10
[68,] 1.000000e+00 3.988490e-11 1.994245e-11
[69,] 1.000000e+00 1.168076e-10 5.840382e-11
[70,] 1.000000e+00 4.118489e-10 2.059245e-10
[71,] 1.000000e+00 1.820954e-09 9.104770e-10
[72,] 1.000000e+00 3.748225e-09 1.874113e-09
[73,] 1.000000e+00 1.600048e-08 8.000238e-09
[74,] 1.000000e+00 5.470173e-08 2.735086e-08
[75,] 9.999999e-01 1.961843e-07 9.809217e-08
[76,] 9.999996e-01 8.391787e-07 4.195894e-07
[77,] 9.999986e-01 2.848075e-06 1.424037e-06
[78,] 9.999950e-01 1.007636e-05 5.038179e-06
[79,] 9.999842e-01 3.161143e-05 1.580572e-05
[80,] 9.999575e-01 8.502273e-05 4.251137e-05
[81,] 9.999246e-01 1.507184e-04 7.535920e-05
[82,] 9.997322e-01 5.355024e-04 2.677512e-04
[83,] 9.992854e-01 1.429290e-03 7.146450e-04
[84,] 9.988049e-01 2.390245e-03 1.195122e-03
[85,] 9.964528e-01 7.094361e-03 3.547181e-03
[86,] 9.865260e-01 2.694795e-02 1.347397e-02
[87,] 9.646131e-01 7.077379e-02 3.538690e-02
> postscript(file="/var/www/html/rcomp/tmp/1m6m01291648666.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2ffm31291648666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3ffm31291648666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ffm31291648666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/57o3o1291648666.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
950.718853 802.655406 486.686767 195.486754 -10.747942 -28.525816
7 8 9 10 11 12
-292.022665 -284.316637 -95.397445 -168.908939 -107.682663 -207.301210
13 14 15 16 17 18
-628.504786 -683.539216 -643.297748 -641.629357 -263.445912 -142.062873
19 20 21 22 23 24
-65.997188 -7.690623 -143.379101 81.325067 131.277678 110.640803
25 26 27 28 29 30
-64.534388 -114.003713 78.295532 9.461615 -139.106485 -4.796052
31 32 33 34 35 36
228.283575 266.528734 483.224039 342.592151 179.635816 184.965437
37 38 39 40 41 42
203.059650 231.466982 -62.098355 131.802015 155.418861 206.609288
43 44 45 46 47 48
294.145853 223.731918 234.917231 148.659579 59.416450 137.644028
49 50 51 52 53 54
-47.002501 -72.992979 -179.591013 -57.356973 -47.993370 -157.991380
55 56 57 58 59 60
-246.717746 -192.721522 -323.223309 -476.356679 -320.469588 -366.906742
61 62 63 64 65 66
-583.586672 -550.451069 -495.121698 -427.621429 -261.465503 -291.958519
67 68 69 70 71 72
-217.627977 -102.727676 -102.378416 67.196440 23.047302 42.694965
73 74 75 76 77 78
-60.265770 -72.683142 26.948567 222.493831 171.788935 244.817745
79 80 81 82 83 84
266.412178 309.859476 215.224421 246.289854 130.048378 221.066190
85 86 87 88 89 90
197.435634 261.461020 304.378626 260.470528 289.743320 273.541908
91 92 93 94 95 96
409.070220 405.623657 276.682851 208.912722 287.762673 281.606694
97 98 99 100 101 102
194.063606 154.912398 216.812713 315.804564 195.602754 89.784668
103 104 105 106 107 108
-49.916424 -153.822488 -169.102850 -200.978886 -110.971740 -79.862969
109 110 111 112 113 114
21.386542 -38.316488 176.843368 51.430306 -54.579153 -56.539423
115 116 117 118 119 120
-176.593782 -299.093105 -223.699128 -120.508407 -163.857289 -240.752225
121 122 123 124 125 126
-182.770168 81.490800 90.143241 -60.341853 -35.215506 -132.879547
127 128 129 130 131 132
-149.036046 -165.371733 -152.868293 -128.222904 -108.207018 -83.794970
> postscript(file="/var/www/html/rcomp/tmp/67o3o1291648666.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
0 950.718853 NA
1 802.655406 950.718853
2 486.686767 802.655406
3 195.486754 486.686767
4 -10.747942 195.486754
5 -28.525816 -10.747942
6 -292.022665 -28.525816
7 -284.316637 -292.022665
8 -95.397445 -284.316637
9 -168.908939 -95.397445
10 -107.682663 -168.908939
11 -207.301210 -107.682663
12 -628.504786 -207.301210
13 -683.539216 -628.504786
14 -643.297748 -683.539216
15 -641.629357 -643.297748
16 -263.445912 -641.629357
17 -142.062873 -263.445912
18 -65.997188 -142.062873
19 -7.690623 -65.997188
20 -143.379101 -7.690623
21 81.325067 -143.379101
22 131.277678 81.325067
23 110.640803 131.277678
24 -64.534388 110.640803
25 -114.003713 -64.534388
26 78.295532 -114.003713
27 9.461615 78.295532
28 -139.106485 9.461615
29 -4.796052 -139.106485
30 228.283575 -4.796052
31 266.528734 228.283575
32 483.224039 266.528734
33 342.592151 483.224039
34 179.635816 342.592151
35 184.965437 179.635816
36 203.059650 184.965437
37 231.466982 203.059650
38 -62.098355 231.466982
39 131.802015 -62.098355
40 155.418861 131.802015
41 206.609288 155.418861
42 294.145853 206.609288
43 223.731918 294.145853
44 234.917231 223.731918
45 148.659579 234.917231
46 59.416450 148.659579
47 137.644028 59.416450
48 -47.002501 137.644028
49 -72.992979 -47.002501
50 -179.591013 -72.992979
51 -57.356973 -179.591013
52 -47.993370 -57.356973
53 -157.991380 -47.993370
54 -246.717746 -157.991380
55 -192.721522 -246.717746
56 -323.223309 -192.721522
57 -476.356679 -323.223309
58 -320.469588 -476.356679
59 -366.906742 -320.469588
60 -583.586672 -366.906742
61 -550.451069 -583.586672
62 -495.121698 -550.451069
63 -427.621429 -495.121698
64 -261.465503 -427.621429
65 -291.958519 -261.465503
66 -217.627977 -291.958519
67 -102.727676 -217.627977
68 -102.378416 -102.727676
69 67.196440 -102.378416
70 23.047302 67.196440
71 42.694965 23.047302
72 -60.265770 42.694965
73 -72.683142 -60.265770
74 26.948567 -72.683142
75 222.493831 26.948567
76 171.788935 222.493831
77 244.817745 171.788935
78 266.412178 244.817745
79 309.859476 266.412178
80 215.224421 309.859476
81 246.289854 215.224421
82 130.048378 246.289854
83 221.066190 130.048378
84 197.435634 221.066190
85 261.461020 197.435634
86 304.378626 261.461020
87 260.470528 304.378626
88 289.743320 260.470528
89 273.541908 289.743320
90 409.070220 273.541908
91 405.623657 409.070220
92 276.682851 405.623657
93 208.912722 276.682851
94 287.762673 208.912722
95 281.606694 287.762673
96 194.063606 281.606694
97 154.912398 194.063606
98 216.812713 154.912398
99 315.804564 216.812713
100 195.602754 315.804564
101 89.784668 195.602754
102 -49.916424 89.784668
103 -153.822488 -49.916424
104 -169.102850 -153.822488
105 -200.978886 -169.102850
106 -110.971740 -200.978886
107 -79.862969 -110.971740
108 21.386542 -79.862969
109 -38.316488 21.386542
110 176.843368 -38.316488
111 51.430306 176.843368
112 -54.579153 51.430306
113 -56.539423 -54.579153
114 -176.593782 -56.539423
115 -299.093105 -176.593782
116 -223.699128 -299.093105
117 -120.508407 -223.699128
118 -163.857289 -120.508407
119 -240.752225 -163.857289
120 -182.770168 -240.752225
121 81.490800 -182.770168
122 90.143241 81.490800
123 -60.341853 90.143241
124 -35.215506 -60.341853
125 -132.879547 -35.215506
126 -149.036046 -132.879547
127 -165.371733 -149.036046
128 -152.868293 -165.371733
129 -128.222904 -152.868293
130 -108.207018 -128.222904
131 -83.794970 -108.207018
132 NA -83.794970
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 802.655406 950.718853
[2,] 486.686767 802.655406
[3,] 195.486754 486.686767
[4,] -10.747942 195.486754
[5,] -28.525816 -10.747942
[6,] -292.022665 -28.525816
[7,] -284.316637 -292.022665
[8,] -95.397445 -284.316637
[9,] -168.908939 -95.397445
[10,] -107.682663 -168.908939
[11,] -207.301210 -107.682663
[12,] -628.504786 -207.301210
[13,] -683.539216 -628.504786
[14,] -643.297748 -683.539216
[15,] -641.629357 -643.297748
[16,] -263.445912 -641.629357
[17,] -142.062873 -263.445912
[18,] -65.997188 -142.062873
[19,] -7.690623 -65.997188
[20,] -143.379101 -7.690623
[21,] 81.325067 -143.379101
[22,] 131.277678 81.325067
[23,] 110.640803 131.277678
[24,] -64.534388 110.640803
[25,] -114.003713 -64.534388
[26,] 78.295532 -114.003713
[27,] 9.461615 78.295532
[28,] -139.106485 9.461615
[29,] -4.796052 -139.106485
[30,] 228.283575 -4.796052
[31,] 266.528734 228.283575
[32,] 483.224039 266.528734
[33,] 342.592151 483.224039
[34,] 179.635816 342.592151
[35,] 184.965437 179.635816
[36,] 203.059650 184.965437
[37,] 231.466982 203.059650
[38,] -62.098355 231.466982
[39,] 131.802015 -62.098355
[40,] 155.418861 131.802015
[41,] 206.609288 155.418861
[42,] 294.145853 206.609288
[43,] 223.731918 294.145853
[44,] 234.917231 223.731918
[45,] 148.659579 234.917231
[46,] 59.416450 148.659579
[47,] 137.644028 59.416450
[48,] -47.002501 137.644028
[49,] -72.992979 -47.002501
[50,] -179.591013 -72.992979
[51,] -57.356973 -179.591013
[52,] -47.993370 -57.356973
[53,] -157.991380 -47.993370
[54,] -246.717746 -157.991380
[55,] -192.721522 -246.717746
[56,] -323.223309 -192.721522
[57,] -476.356679 -323.223309
[58,] -320.469588 -476.356679
[59,] -366.906742 -320.469588
[60,] -583.586672 -366.906742
[61,] -550.451069 -583.586672
[62,] -495.121698 -550.451069
[63,] -427.621429 -495.121698
[64,] -261.465503 -427.621429
[65,] -291.958519 -261.465503
[66,] -217.627977 -291.958519
[67,] -102.727676 -217.627977
[68,] -102.378416 -102.727676
[69,] 67.196440 -102.378416
[70,] 23.047302 67.196440
[71,] 42.694965 23.047302
[72,] -60.265770 42.694965
[73,] -72.683142 -60.265770
[74,] 26.948567 -72.683142
[75,] 222.493831 26.948567
[76,] 171.788935 222.493831
[77,] 244.817745 171.788935
[78,] 266.412178 244.817745
[79,] 309.859476 266.412178
[80,] 215.224421 309.859476
[81,] 246.289854 215.224421
[82,] 130.048378 246.289854
[83,] 221.066190 130.048378
[84,] 197.435634 221.066190
[85,] 261.461020 197.435634
[86,] 304.378626 261.461020
[87,] 260.470528 304.378626
[88,] 289.743320 260.470528
[89,] 273.541908 289.743320
[90,] 409.070220 273.541908
[91,] 405.623657 409.070220
[92,] 276.682851 405.623657
[93,] 208.912722 276.682851
[94,] 287.762673 208.912722
[95,] 281.606694 287.762673
[96,] 194.063606 281.606694
[97,] 154.912398 194.063606
[98,] 216.812713 154.912398
[99,] 315.804564 216.812713
[100,] 195.602754 315.804564
[101,] 89.784668 195.602754
[102,] -49.916424 89.784668
[103,] -153.822488 -49.916424
[104,] -169.102850 -153.822488
[105,] -200.978886 -169.102850
[106,] -110.971740 -200.978886
[107,] -79.862969 -110.971740
[108,] 21.386542 -79.862969
[109,] -38.316488 21.386542
[110,] 176.843368 -38.316488
[111,] 51.430306 176.843368
[112,] -54.579153 51.430306
[113,] -56.539423 -54.579153
[114,] -176.593782 -56.539423
[115,] -299.093105 -176.593782
[116,] -223.699128 -299.093105
[117,] -120.508407 -223.699128
[118,] -163.857289 -120.508407
[119,] -240.752225 -163.857289
[120,] -182.770168 -240.752225
[121,] 81.490800 -182.770168
[122,] 90.143241 81.490800
[123,] -60.341853 90.143241
[124,] -35.215506 -60.341853
[125,] -132.879547 -35.215506
[126,] -149.036046 -132.879547
[127,] -165.371733 -149.036046
[128,] -152.868293 -165.371733
[129,] -128.222904 -152.868293
[130,] -108.207018 -128.222904
[131,] -83.794970 -108.207018
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 802.655406 950.718853
2 486.686767 802.655406
3 195.486754 486.686767
4 -10.747942 195.486754
5 -28.525816 -10.747942
6 -292.022665 -28.525816
7 -284.316637 -292.022665
8 -95.397445 -284.316637
9 -168.908939 -95.397445
10 -107.682663 -168.908939
11 -207.301210 -107.682663
12 -628.504786 -207.301210
13 -683.539216 -628.504786
14 -643.297748 -683.539216
15 -641.629357 -643.297748
16 -263.445912 -641.629357
17 -142.062873 -263.445912
18 -65.997188 -142.062873
19 -7.690623 -65.997188
20 -143.379101 -7.690623
21 81.325067 -143.379101
22 131.277678 81.325067
23 110.640803 131.277678
24 -64.534388 110.640803
25 -114.003713 -64.534388
26 78.295532 -114.003713
27 9.461615 78.295532
28 -139.106485 9.461615
29 -4.796052 -139.106485
30 228.283575 -4.796052
31 266.528734 228.283575
32 483.224039 266.528734
33 342.592151 483.224039
34 179.635816 342.592151
35 184.965437 179.635816
36 203.059650 184.965437
37 231.466982 203.059650
38 -62.098355 231.466982
39 131.802015 -62.098355
40 155.418861 131.802015
41 206.609288 155.418861
42 294.145853 206.609288
43 223.731918 294.145853
44 234.917231 223.731918
45 148.659579 234.917231
46 59.416450 148.659579
47 137.644028 59.416450
48 -47.002501 137.644028
49 -72.992979 -47.002501
50 -179.591013 -72.992979
51 -57.356973 -179.591013
52 -47.993370 -57.356973
53 -157.991380 -47.993370
54 -246.717746 -157.991380
55 -192.721522 -246.717746
56 -323.223309 -192.721522
57 -476.356679 -323.223309
58 -320.469588 -476.356679
59 -366.906742 -320.469588
60 -583.586672 -366.906742
61 -550.451069 -583.586672
62 -495.121698 -550.451069
63 -427.621429 -495.121698
64 -261.465503 -427.621429
65 -291.958519 -261.465503
66 -217.627977 -291.958519
67 -102.727676 -217.627977
68 -102.378416 -102.727676
69 67.196440 -102.378416
70 23.047302 67.196440
71 42.694965 23.047302
72 -60.265770 42.694965
73 -72.683142 -60.265770
74 26.948567 -72.683142
75 222.493831 26.948567
76 171.788935 222.493831
77 244.817745 171.788935
78 266.412178 244.817745
79 309.859476 266.412178
80 215.224421 309.859476
81 246.289854 215.224421
82 130.048378 246.289854
83 221.066190 130.048378
84 197.435634 221.066190
85 261.461020 197.435634
86 304.378626 261.461020
87 260.470528 304.378626
88 289.743320 260.470528
89 273.541908 289.743320
90 409.070220 273.541908
91 405.623657 409.070220
92 276.682851 405.623657
93 208.912722 276.682851
94 287.762673 208.912722
95 281.606694 287.762673
96 194.063606 281.606694
97 154.912398 194.063606
98 216.812713 154.912398
99 315.804564 216.812713
100 195.602754 315.804564
101 89.784668 195.602754
102 -49.916424 89.784668
103 -153.822488 -49.916424
104 -169.102850 -153.822488
105 -200.978886 -169.102850
106 -110.971740 -200.978886
107 -79.862969 -110.971740
108 21.386542 -79.862969
109 -38.316488 21.386542
110 176.843368 -38.316488
111 51.430306 176.843368
112 -54.579153 51.430306
113 -56.539423 -54.579153
114 -176.593782 -56.539423
115 -299.093105 -176.593782
116 -223.699128 -299.093105
117 -120.508407 -223.699128
118 -163.857289 -120.508407
119 -240.752225 -163.857289
120 -182.770168 -240.752225
121 81.490800 -182.770168
122 90.143241 81.490800
123 -60.341853 90.143241
124 -35.215506 -60.341853
125 -132.879547 -35.215506
126 -149.036046 -132.879547
127 -165.371733 -149.036046
128 -152.868293 -165.371733
129 -128.222904 -152.868293
130 -108.207018 -128.222904
131 -83.794970 -108.207018
> 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/7ixkr1291648666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8ixkr1291648666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9so1t1291648666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10so1t1291648666.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11wpiz1291648666.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/12zqgn1291648666.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/13vzwe1291648666.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/14hiu21291648666.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/15kitq1291648666.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/16njre1291648666.tab")
+ }
>
> try(system("convert tmp/1m6m01291648666.ps tmp/1m6m01291648666.png",intern=TRUE))
character(0)
> try(system("convert tmp/2ffm31291648666.ps tmp/2ffm31291648666.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ffm31291648666.ps tmp/3ffm31291648666.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ffm31291648666.ps tmp/4ffm31291648666.png",intern=TRUE))
character(0)
> try(system("convert tmp/57o3o1291648666.ps tmp/57o3o1291648666.png",intern=TRUE))
character(0)
> try(system("convert tmp/67o3o1291648666.ps tmp/67o3o1291648666.png",intern=TRUE))
character(0)
> try(system("convert tmp/7ixkr1291648666.ps tmp/7ixkr1291648666.png",intern=TRUE))
character(0)
> try(system("convert tmp/8ixkr1291648666.ps tmp/8ixkr1291648666.png",intern=TRUE))
character(0)
> try(system("convert tmp/9so1t1291648666.ps tmp/9so1t1291648666.png",intern=TRUE))
character(0)
> try(system("convert tmp/10so1t1291648666.ps tmp/10so1t1291648666.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.835 1.705 8.970