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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+ ,22052
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+ ,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 = 'No 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
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 1
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 1
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 1
36 0
37 0
38 0
39 0
40 0
41 0
42 0
43 0
44 0
45 0
46 0
47 1
48 0
49 0
50 0
51 0
52 0
53 0
54 0
55 0
56 0
57 0
58 0
59 1
60 0
61 0
62 0
63 0
64 0
65 0
66 0
67 0
68 0
69 0
70 0
71 1
72 0
73 0
74 0
75 0
76 0
77 0
78 0
79 0
80 0
81 0
82 0
83 1
84 0
85 0
86 0
87 0
88 0
89 0
90 0
91 0
92 0
93 0
94 0
95 1
96 0
97 0
98 0
99 0
100 0
101 0
102 0
103 0
104 0
105 0
106 0
107 1
108 0
109 0
110 0
111 0
112 0
113 0
114 0
115 0
116 0
117 0
118 0
119 1
120 0
121 0
122 0
123 0
124 0
125 0
126 0
127 0
128 0
129 0
130 0
131 1
132 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) Nikkei DJ_Indust
-1.619e+03 9.709e-02 3.776e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
2.244e-02 -1.200e+01 1.320e+01
Alg_consumptie_index_BE Gem_rente_kasbon_5j M1
3.516e+01 -2.812e+02 1.017e+02
M2 M3 M4
1.117e+02 6.696e+01 2.708e+01
M5 M6 M7
9.196e+00 -8.268e+00 4.352e+01
M8 M9 M10
5.480e+01 9.035e+01 1.546e+02
M11
8.593e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-551.635 -138.243 3.624 174.296 659.791
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.619e+03 3.608e+02 -4.488 1.74e-05 ***
Nikkei 9.709e-02 1.582e-02 6.137 1.28e-08 ***
DJ_Indust 3.776e-01 3.600e-02 10.490 < 2e-16 ***
Goudprijs 2.244e-02 8.695e-03 2.581 0.0111 *
Conjunct_Seizoenzuiver -1.200e+01 7.307e+00 -1.642 0.1034
Cons_vertrouw 1.320e+01 6.992e+00 1.887 0.0617 .
Alg_consumptie_index_BE 3.516e+01 2.431e+01 1.446 0.1509
Gem_rente_kasbon_5j -2.812e+02 5.683e+01 -4.947 2.64e-06 ***
M1 1.017e+02 1.168e+02 0.870 0.3860
M2 1.117e+02 1.179e+02 0.948 0.3454
M3 6.696e+01 1.174e+02 0.570 0.5696
M4 2.708e+01 1.176e+02 0.230 0.8183
M5 9.196e+00 1.154e+02 0.080 0.9366
M6 -8.268e+00 1.149e+02 -0.072 0.9428
M7 4.352e+01 1.147e+02 0.379 0.7052
M8 5.480e+01 1.146e+02 0.478 0.6335
M9 9.035e+01 1.147e+02 0.788 0.4324
M10 1.546e+02 1.151e+02 1.344 0.1818
M11 8.593e+01 1.146e+02 0.750 0.4547
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 268.1 on 113 degrees of freedom
Multiple R-squared: 0.8906, Adjusted R-squared: 0.8732
F-statistic: 51.11 on 18 and 113 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,] 0.054568282 1.091366e-01 9.454317e-01
[2,] 0.060358556 1.207171e-01 9.396414e-01
[3,] 0.116817775 2.336356e-01 8.831822e-01
[4,] 0.068295107 1.365902e-01 9.317049e-01
[5,] 0.048434603 9.686921e-02 9.515654e-01
[6,] 0.023910704 4.782141e-02 9.760893e-01
[7,] 0.032178799 6.435760e-02 9.678212e-01
[8,] 0.035143795 7.028759e-02 9.648562e-01
[9,] 0.021622016 4.324403e-02 9.783780e-01
[10,] 0.011545940 2.309188e-02 9.884541e-01
[11,] 0.006500092 1.300018e-02 9.934999e-01
[12,] 0.009966632 1.993326e-02 9.900334e-01
[13,] 0.006900283 1.380057e-02 9.930997e-01
[14,] 0.003865411 7.730821e-03 9.961346e-01
[15,] 0.002074454 4.148907e-03 9.979255e-01
[16,] 0.001106375 2.212749e-03 9.988936e-01
[17,] 0.002490771 4.981542e-03 9.975092e-01
[18,] 0.004451242 8.902484e-03 9.955488e-01
[19,] 0.018103192 3.620638e-02 9.818968e-01
[20,] 0.015008151 3.001630e-02 9.849918e-01
[21,] 0.017015770 3.403154e-02 9.829842e-01
[22,] 0.027288750 5.457750e-02 9.727113e-01
[23,] 0.053923598 1.078472e-01 9.460764e-01
[24,] 0.108917190 2.178344e-01 8.910828e-01
[25,] 0.324582121 6.491642e-01 6.754179e-01
[26,] 0.511228729 9.775425e-01 4.887713e-01
[27,] 0.544241753 9.115165e-01 4.557582e-01
[28,] 0.512953272 9.740935e-01 4.870467e-01
[29,] 0.458804101 9.176082e-01 5.411959e-01
[30,] 0.404892387 8.097848e-01 5.951076e-01
[31,] 0.553447766 8.931045e-01 4.465522e-01
[32,] 0.645364214 7.092716e-01 3.546358e-01
[33,] 0.819990638 3.600187e-01 1.800094e-01
[34,] 0.834594008 3.308120e-01 1.654060e-01
[35,] 0.806931875 3.861363e-01 1.930681e-01
[36,] 0.825645828 3.487083e-01 1.743542e-01
[37,] 0.868560164 2.628797e-01 1.314398e-01
[38,] 0.848632483 3.027350e-01 1.513675e-01
[39,] 0.823936232 3.521275e-01 1.760638e-01
[40,] 0.855106072 2.897879e-01 1.448939e-01
[41,] 0.869927347 2.601453e-01 1.300727e-01
[42,] 0.937484967 1.250301e-01 6.251503e-02
[43,] 0.994615026 1.076995e-02 5.384974e-03
[44,] 0.998719058 2.561884e-03 1.280942e-03
[45,] 0.999640652 7.186951e-04 3.593476e-04
[46,] 0.999951874 9.625266e-05 4.812633e-05
[47,] 0.999977336 4.532760e-05 2.266380e-05
[48,] 0.999987186 2.562753e-05 1.281376e-05
[49,] 0.999992309 1.538287e-05 7.691433e-06
[50,] 0.999994256 1.148796e-05 5.743982e-06
[51,] 0.999994444 1.111152e-05 5.555762e-06
[52,] 0.999997303 5.394023e-06 2.697011e-06
[53,] 0.999999042 1.916105e-06 9.580524e-07
[54,] 0.999999901 1.982614e-07 9.913068e-08
[55,] 0.999999953 9.311473e-08 4.655736e-08
[56,] 0.999999990 1.918899e-08 9.594493e-09
[57,] 0.999999993 1.342018e-08 6.710089e-09
[58,] 0.999999990 2.072385e-08 1.036192e-08
[59,] 0.999999991 1.787319e-08 8.936593e-09
[60,] 0.999999986 2.760664e-08 1.380332e-08
[61,] 0.999999972 5.561066e-08 2.780533e-08
[62,] 0.999999963 7.327946e-08 3.663973e-08
[63,] 0.999999939 1.216589e-07 6.082943e-08
[64,] 0.999999941 1.176040e-07 5.880200e-08
[65,] 0.999999972 5.699553e-08 2.849777e-08
[66,] 0.999999993 1.496736e-08 7.483679e-09
[67,] 0.999999999 2.035605e-09 1.017802e-09
[68,] 0.999999999 1.057320e-09 5.286601e-10
[69,] 1.000000000 3.654288e-10 1.827144e-10
[70,] 0.999999999 1.290502e-09 6.452512e-10
[71,] 0.999999999 1.095966e-09 5.479828e-10
[72,] 0.999999998 4.332046e-09 2.166023e-09
[73,] 0.999999994 1.103146e-08 5.515732e-09
[74,] 0.999999979 4.228019e-08 2.114009e-08
[75,] 0.999999943 1.146865e-07 5.734325e-08
[76,] 0.999999776 4.487987e-07 2.243993e-07
[77,] 0.999999693 6.143173e-07 3.071586e-07
[78,] 0.999999806 3.870777e-07 1.935388e-07
[79,] 0.999999182 1.635188e-06 8.175939e-07
[80,] 0.999996761 6.477264e-06 3.238632e-06
[81,] 0.999990162 1.967646e-05 9.838228e-06
[82,] 0.999965206 6.958709e-05 3.479355e-05
[83,] 0.999878831 2.423383e-04 1.211692e-04
[84,] 0.999565086 8.698270e-04 4.349135e-04
[85,] 0.999004154 1.991691e-03 9.958457e-04
[86,] 0.997177660 5.644679e-03 2.822340e-03
[87,] 0.991346273 1.730745e-02 8.653727e-03
[88,] 0.993063062 1.387388e-02 6.936938e-03
[89,] 0.984626325 3.074735e-02 1.537367e-02
> postscript(file="/var/www/html/rcomp/tmp/1l8j91291653400.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2l8j91291653400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3wz1u1291653400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4wz1u1291653400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5wz1u1291653400.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
659.790673 579.450551 261.836382 -90.795957 -286.644562 -20.830063
7 8 9 10 11 12
-323.825620 -222.703114 -44.512367 -90.181870 -135.984295 -156.541244
13 14 15 16 17 18
-506.625073 -510.800514 -511.271813 -551.634598 -118.179718 -92.596268
19 20 21 22 23 24
-58.280154 -32.492098 -81.633087 92.195804 144.376050 44.774889
25 26 27 28 29 30
-42.803194 5.008578 168.583647 54.072764 -145.017326 -100.960985
31 32 33 34 35 36
117.729247 232.283753 471.803108 369.721408 252.492117 239.208557
37 38 39 40 41 42
171.335368 253.774764 26.003260 338.708649 349.548249 398.769649
43 44 45 46 47 48
426.776503 285.827573 269.909669 163.733197 150.713917 251.421434
49 50 51 52 53 54
25.126092 12.227884 -43.981651 62.241510 -106.955061 -409.359708
55 56 57 58 59 60
-406.589418 -284.401406 -490.879948 -395.092941 -351.073021 -370.994400
61 62 63 64 65 66
-532.003253 -457.475565 -417.908595 -442.748226 -217.268260 -372.827972
67 68 69 70 71 72
-202.887114 -165.713238 -115.041677 46.375257 40.714286 56.830731
73 74 75 76 77 78
27.969696 -88.323509 -70.683686 183.178551 204.766779 132.240324
79 80 81 82 83 84
51.576805 99.158423 91.881079 40.846552 -5.757797 2.239469
85 86 87 88 89 90
-18.677543 71.630787 244.237502 186.825951 202.482506 256.798232
91 92 93 94 95 96
345.014805 347.854469 294.027438 150.063764 201.017458 380.122837
97 98 99 100 101 102
258.481720 239.838400 358.077082 388.091082 276.864912 284.917990
103 104 105 106 107 108
164.802532 82.388306 -8.850345 -203.302982 -44.909855 -124.048545
109 110 111 112 113 114
27.258091 -151.086916 -92.391919 -92.583105 -155.684446 -24.333118
115 116 117 118 119 120
-68.341002 -110.215288 -191.632416 35.207629 118.256317 5.452108
121 122 123 124 125 126
-69.852576 45.755541 77.499791 -35.356620 -3.913073 -51.818083
127 128 129 130 131 132
-45.976584 -231.987381 -195.071453 -209.565819 -369.845176 -328.465837
> postscript(file="/var/www/html/rcomp/tmp/6780x1291653400.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
0 659.790673 NA
1 579.450551 659.790673
2 261.836382 579.450551
3 -90.795957 261.836382
4 -286.644562 -90.795957
5 -20.830063 -286.644562
6 -323.825620 -20.830063
7 -222.703114 -323.825620
8 -44.512367 -222.703114
9 -90.181870 -44.512367
10 -135.984295 -90.181870
11 -156.541244 -135.984295
12 -506.625073 -156.541244
13 -510.800514 -506.625073
14 -511.271813 -510.800514
15 -551.634598 -511.271813
16 -118.179718 -551.634598
17 -92.596268 -118.179718
18 -58.280154 -92.596268
19 -32.492098 -58.280154
20 -81.633087 -32.492098
21 92.195804 -81.633087
22 144.376050 92.195804
23 44.774889 144.376050
24 -42.803194 44.774889
25 5.008578 -42.803194
26 168.583647 5.008578
27 54.072764 168.583647
28 -145.017326 54.072764
29 -100.960985 -145.017326
30 117.729247 -100.960985
31 232.283753 117.729247
32 471.803108 232.283753
33 369.721408 471.803108
34 252.492117 369.721408
35 239.208557 252.492117
36 171.335368 239.208557
37 253.774764 171.335368
38 26.003260 253.774764
39 338.708649 26.003260
40 349.548249 338.708649
41 398.769649 349.548249
42 426.776503 398.769649
43 285.827573 426.776503
44 269.909669 285.827573
45 163.733197 269.909669
46 150.713917 163.733197
47 251.421434 150.713917
48 25.126092 251.421434
49 12.227884 25.126092
50 -43.981651 12.227884
51 62.241510 -43.981651
52 -106.955061 62.241510
53 -409.359708 -106.955061
54 -406.589418 -409.359708
55 -284.401406 -406.589418
56 -490.879948 -284.401406
57 -395.092941 -490.879948
58 -351.073021 -395.092941
59 -370.994400 -351.073021
60 -532.003253 -370.994400
61 -457.475565 -532.003253
62 -417.908595 -457.475565
63 -442.748226 -417.908595
64 -217.268260 -442.748226
65 -372.827972 -217.268260
66 -202.887114 -372.827972
67 -165.713238 -202.887114
68 -115.041677 -165.713238
69 46.375257 -115.041677
70 40.714286 46.375257
71 56.830731 40.714286
72 27.969696 56.830731
73 -88.323509 27.969696
74 -70.683686 -88.323509
75 183.178551 -70.683686
76 204.766779 183.178551
77 132.240324 204.766779
78 51.576805 132.240324
79 99.158423 51.576805
80 91.881079 99.158423
81 40.846552 91.881079
82 -5.757797 40.846552
83 2.239469 -5.757797
84 -18.677543 2.239469
85 71.630787 -18.677543
86 244.237502 71.630787
87 186.825951 244.237502
88 202.482506 186.825951
89 256.798232 202.482506
90 345.014805 256.798232
91 347.854469 345.014805
92 294.027438 347.854469
93 150.063764 294.027438
94 201.017458 150.063764
95 380.122837 201.017458
96 258.481720 380.122837
97 239.838400 258.481720
98 358.077082 239.838400
99 388.091082 358.077082
100 276.864912 388.091082
101 284.917990 276.864912
102 164.802532 284.917990
103 82.388306 164.802532
104 -8.850345 82.388306
105 -203.302982 -8.850345
106 -44.909855 -203.302982
107 -124.048545 -44.909855
108 27.258091 -124.048545
109 -151.086916 27.258091
110 -92.391919 -151.086916
111 -92.583105 -92.391919
112 -155.684446 -92.583105
113 -24.333118 -155.684446
114 -68.341002 -24.333118
115 -110.215288 -68.341002
116 -191.632416 -110.215288
117 35.207629 -191.632416
118 118.256317 35.207629
119 5.452108 118.256317
120 -69.852576 5.452108
121 45.755541 -69.852576
122 77.499791 45.755541
123 -35.356620 77.499791
124 -3.913073 -35.356620
125 -51.818083 -3.913073
126 -45.976584 -51.818083
127 -231.987381 -45.976584
128 -195.071453 -231.987381
129 -209.565819 -195.071453
130 -369.845176 -209.565819
131 -328.465837 -369.845176
132 NA -328.465837
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 579.450551 659.790673
[2,] 261.836382 579.450551
[3,] -90.795957 261.836382
[4,] -286.644562 -90.795957
[5,] -20.830063 -286.644562
[6,] -323.825620 -20.830063
[7,] -222.703114 -323.825620
[8,] -44.512367 -222.703114
[9,] -90.181870 -44.512367
[10,] -135.984295 -90.181870
[11,] -156.541244 -135.984295
[12,] -506.625073 -156.541244
[13,] -510.800514 -506.625073
[14,] -511.271813 -510.800514
[15,] -551.634598 -511.271813
[16,] -118.179718 -551.634598
[17,] -92.596268 -118.179718
[18,] -58.280154 -92.596268
[19,] -32.492098 -58.280154
[20,] -81.633087 -32.492098
[21,] 92.195804 -81.633087
[22,] 144.376050 92.195804
[23,] 44.774889 144.376050
[24,] -42.803194 44.774889
[25,] 5.008578 -42.803194
[26,] 168.583647 5.008578
[27,] 54.072764 168.583647
[28,] -145.017326 54.072764
[29,] -100.960985 -145.017326
[30,] 117.729247 -100.960985
[31,] 232.283753 117.729247
[32,] 471.803108 232.283753
[33,] 369.721408 471.803108
[34,] 252.492117 369.721408
[35,] 239.208557 252.492117
[36,] 171.335368 239.208557
[37,] 253.774764 171.335368
[38,] 26.003260 253.774764
[39,] 338.708649 26.003260
[40,] 349.548249 338.708649
[41,] 398.769649 349.548249
[42,] 426.776503 398.769649
[43,] 285.827573 426.776503
[44,] 269.909669 285.827573
[45,] 163.733197 269.909669
[46,] 150.713917 163.733197
[47,] 251.421434 150.713917
[48,] 25.126092 251.421434
[49,] 12.227884 25.126092
[50,] -43.981651 12.227884
[51,] 62.241510 -43.981651
[52,] -106.955061 62.241510
[53,] -409.359708 -106.955061
[54,] -406.589418 -409.359708
[55,] -284.401406 -406.589418
[56,] -490.879948 -284.401406
[57,] -395.092941 -490.879948
[58,] -351.073021 -395.092941
[59,] -370.994400 -351.073021
[60,] -532.003253 -370.994400
[61,] -457.475565 -532.003253
[62,] -417.908595 -457.475565
[63,] -442.748226 -417.908595
[64,] -217.268260 -442.748226
[65,] -372.827972 -217.268260
[66,] -202.887114 -372.827972
[67,] -165.713238 -202.887114
[68,] -115.041677 -165.713238
[69,] 46.375257 -115.041677
[70,] 40.714286 46.375257
[71,] 56.830731 40.714286
[72,] 27.969696 56.830731
[73,] -88.323509 27.969696
[74,] -70.683686 -88.323509
[75,] 183.178551 -70.683686
[76,] 204.766779 183.178551
[77,] 132.240324 204.766779
[78,] 51.576805 132.240324
[79,] 99.158423 51.576805
[80,] 91.881079 99.158423
[81,] 40.846552 91.881079
[82,] -5.757797 40.846552
[83,] 2.239469 -5.757797
[84,] -18.677543 2.239469
[85,] 71.630787 -18.677543
[86,] 244.237502 71.630787
[87,] 186.825951 244.237502
[88,] 202.482506 186.825951
[89,] 256.798232 202.482506
[90,] 345.014805 256.798232
[91,] 347.854469 345.014805
[92,] 294.027438 347.854469
[93,] 150.063764 294.027438
[94,] 201.017458 150.063764
[95,] 380.122837 201.017458
[96,] 258.481720 380.122837
[97,] 239.838400 258.481720
[98,] 358.077082 239.838400
[99,] 388.091082 358.077082
[100,] 276.864912 388.091082
[101,] 284.917990 276.864912
[102,] 164.802532 284.917990
[103,] 82.388306 164.802532
[104,] -8.850345 82.388306
[105,] -203.302982 -8.850345
[106,] -44.909855 -203.302982
[107,] -124.048545 -44.909855
[108,] 27.258091 -124.048545
[109,] -151.086916 27.258091
[110,] -92.391919 -151.086916
[111,] -92.583105 -92.391919
[112,] -155.684446 -92.583105
[113,] -24.333118 -155.684446
[114,] -68.341002 -24.333118
[115,] -110.215288 -68.341002
[116,] -191.632416 -110.215288
[117,] 35.207629 -191.632416
[118,] 118.256317 35.207629
[119,] 5.452108 118.256317
[120,] -69.852576 5.452108
[121,] 45.755541 -69.852576
[122,] 77.499791 45.755541
[123,] -35.356620 77.499791
[124,] -3.913073 -35.356620
[125,] -51.818083 -3.913073
[126,] -45.976584 -51.818083
[127,] -231.987381 -45.976584
[128,] -195.071453 -231.987381
[129,] -209.565819 -195.071453
[130,] -369.845176 -209.565819
[131,] -328.465837 -369.845176
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 579.450551 659.790673
2 261.836382 579.450551
3 -90.795957 261.836382
4 -286.644562 -90.795957
5 -20.830063 -286.644562
6 -323.825620 -20.830063
7 -222.703114 -323.825620
8 -44.512367 -222.703114
9 -90.181870 -44.512367
10 -135.984295 -90.181870
11 -156.541244 -135.984295
12 -506.625073 -156.541244
13 -510.800514 -506.625073
14 -511.271813 -510.800514
15 -551.634598 -511.271813
16 -118.179718 -551.634598
17 -92.596268 -118.179718
18 -58.280154 -92.596268
19 -32.492098 -58.280154
20 -81.633087 -32.492098
21 92.195804 -81.633087
22 144.376050 92.195804
23 44.774889 144.376050
24 -42.803194 44.774889
25 5.008578 -42.803194
26 168.583647 5.008578
27 54.072764 168.583647
28 -145.017326 54.072764
29 -100.960985 -145.017326
30 117.729247 -100.960985
31 232.283753 117.729247
32 471.803108 232.283753
33 369.721408 471.803108
34 252.492117 369.721408
35 239.208557 252.492117
36 171.335368 239.208557
37 253.774764 171.335368
38 26.003260 253.774764
39 338.708649 26.003260
40 349.548249 338.708649
41 398.769649 349.548249
42 426.776503 398.769649
43 285.827573 426.776503
44 269.909669 285.827573
45 163.733197 269.909669
46 150.713917 163.733197
47 251.421434 150.713917
48 25.126092 251.421434
49 12.227884 25.126092
50 -43.981651 12.227884
51 62.241510 -43.981651
52 -106.955061 62.241510
53 -409.359708 -106.955061
54 -406.589418 -409.359708
55 -284.401406 -406.589418
56 -490.879948 -284.401406
57 -395.092941 -490.879948
58 -351.073021 -395.092941
59 -370.994400 -351.073021
60 -532.003253 -370.994400
61 -457.475565 -532.003253
62 -417.908595 -457.475565
63 -442.748226 -417.908595
64 -217.268260 -442.748226
65 -372.827972 -217.268260
66 -202.887114 -372.827972
67 -165.713238 -202.887114
68 -115.041677 -165.713238
69 46.375257 -115.041677
70 40.714286 46.375257
71 56.830731 40.714286
72 27.969696 56.830731
73 -88.323509 27.969696
74 -70.683686 -88.323509
75 183.178551 -70.683686
76 204.766779 183.178551
77 132.240324 204.766779
78 51.576805 132.240324
79 99.158423 51.576805
80 91.881079 99.158423
81 40.846552 91.881079
82 -5.757797 40.846552
83 2.239469 -5.757797
84 -18.677543 2.239469
85 71.630787 -18.677543
86 244.237502 71.630787
87 186.825951 244.237502
88 202.482506 186.825951
89 256.798232 202.482506
90 345.014805 256.798232
91 347.854469 345.014805
92 294.027438 347.854469
93 150.063764 294.027438
94 201.017458 150.063764
95 380.122837 201.017458
96 258.481720 380.122837
97 239.838400 258.481720
98 358.077082 239.838400
99 388.091082 358.077082
100 276.864912 388.091082
101 284.917990 276.864912
102 164.802532 284.917990
103 82.388306 164.802532
104 -8.850345 82.388306
105 -203.302982 -8.850345
106 -44.909855 -203.302982
107 -124.048545 -44.909855
108 27.258091 -124.048545
109 -151.086916 27.258091
110 -92.391919 -151.086916
111 -92.583105 -92.391919
112 -155.684446 -92.583105
113 -24.333118 -155.684446
114 -68.341002 -24.333118
115 -110.215288 -68.341002
116 -191.632416 -110.215288
117 35.207629 -191.632416
118 118.256317 35.207629
119 5.452108 118.256317
120 -69.852576 5.452108
121 45.755541 -69.852576
122 77.499791 45.755541
123 -35.356620 77.499791
124 -3.913073 -35.356620
125 -51.818083 -3.913073
126 -45.976584 -51.818083
127 -231.987381 -45.976584
128 -195.071453 -231.987381
129 -209.565819 -195.071453
130 -369.845176 -209.565819
131 -328.465837 -369.845176
> 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/7hzzi1291653400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8hzzi1291653400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9a9gl1291653400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10a9gl1291653400.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/11e9f91291653400.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/12havf1291653400.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/136ba91291653400.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/14rt9x1291653400.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/15cupk1291653400.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/16gc681291653400.tab")
+ }
>
> try(system("convert tmp/1l8j91291653400.ps tmp/1l8j91291653400.png",intern=TRUE))
character(0)
> try(system("convert tmp/2l8j91291653400.ps tmp/2l8j91291653400.png",intern=TRUE))
character(0)
> try(system("convert tmp/3wz1u1291653400.ps tmp/3wz1u1291653400.png",intern=TRUE))
character(0)
> try(system("convert tmp/4wz1u1291653400.ps tmp/4wz1u1291653400.png",intern=TRUE))
character(0)
> try(system("convert tmp/5wz1u1291653400.ps tmp/5wz1u1291653400.png",intern=TRUE))
character(0)
> try(system("convert tmp/6780x1291653400.ps tmp/6780x1291653400.png",intern=TRUE))
character(0)
> try(system("convert tmp/7hzzi1291653400.ps tmp/7hzzi1291653400.png",intern=TRUE))
character(0)
> try(system("convert tmp/8hzzi1291653400.ps tmp/8hzzi1291653400.png",intern=TRUE))
character(0)
> try(system("convert tmp/9a9gl1291653400.ps tmp/9a9gl1291653400.png",intern=TRUE))
character(0)
> try(system("convert tmp/10a9gl1291653400.ps tmp/10a9gl1291653400.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.834 1.735 9.202