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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+ ,10169.02
+ ,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 = '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_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
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
-2.145e+03 6.766e-02 3.933e-01
Goudprijs Conjunct_Seizoenzuiver Cons_vertrouw
1.740e-03 3.920e+00 -1.124e+01
Alg_consumptie_index_BE Gem_rente_kasbon_1j M1
-2.954e+01 8.815e+00 1.918e+02
M2 M3 M4
2.240e+02 1.716e+02 1.370e+02
M5 M6 M7
7.186e+01 2.826e+01 3.281e+01
M8 M9 M10
4.583e+01 1.047e+02 1.282e+02
M11
8.014e+01
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-653.35 -160.49 -16.28 211.80 905.28
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.145e+03 3.854e+02 -5.566 1.78e-07 ***
Nikkei 6.766e-02 1.782e-02 3.797 0.000237 ***
DJ_Indust 3.933e-01 3.956e-02 9.943 < 2e-16 ***
Goudprijs 1.740e-03 9.630e-03 0.181 0.856957
Conjunct_Seizoenzuiver 3.920e+00 8.166e+00 0.480 0.632088
Cons_vertrouw -1.124e+01 6.989e+00 -1.608 0.110731
Alg_consumptie_index_BE -2.954e+01 3.143e+01 -0.940 0.349294
Gem_rente_kasbon_1j 8.815e+00 4.566e+01 0.193 0.847248
M1 1.918e+02 1.279e+02 1.500 0.136519
M2 2.240e+02 1.288e+02 1.739 0.084837 .
M3 1.716e+02 1.285e+02 1.336 0.184264
M4 1.370e+02 1.287e+02 1.065 0.289344
M5 7.186e+01 1.267e+02 0.567 0.571586
M6 2.826e+01 1.265e+02 0.223 0.823698
M7 3.281e+01 1.265e+02 0.259 0.795829
M8 4.583e+01 1.264e+02 0.363 0.717582
M9 1.047e+02 1.265e+02 0.828 0.409492
M10 1.282e+02 1.269e+02 1.010 0.314444
M11 8.014e+01 1.264e+02 0.634 0.527407
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 295.7 on 113 degrees of freedom
Multiple R-squared: 0.867, Adjusted R-squared: 0.8458
F-statistic: 40.91 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,] 0.05102747 1.020549e-01 9.489725e-01
[2,] 0.08837636 1.767527e-01 9.116236e-01
[3,] 0.12307082 2.461416e-01 8.769292e-01
[4,] 0.07341433 1.468287e-01 9.265857e-01
[5,] 0.05232529 1.046506e-01 9.476747e-01
[6,] 0.02597073 5.194146e-02 9.740293e-01
[7,] 0.07023095 1.404619e-01 9.297691e-01
[8,] 0.08895347 1.779069e-01 9.110465e-01
[9,] 0.08416839 1.683368e-01 9.158316e-01
[10,] 0.05411160 1.082232e-01 9.458884e-01
[11,] 0.03856204 7.712407e-02 9.614380e-01
[12,] 0.08173883 1.634777e-01 9.182612e-01
[13,] 0.06055737 1.211147e-01 9.394426e-01
[14,] 0.04180024 8.360048e-02 9.581998e-01
[15,] 0.03804590 7.609179e-02 9.619541e-01
[16,] 0.02601003 5.202006e-02 9.739900e-01
[17,] 0.02183204 4.366408e-02 9.781680e-01
[18,] 0.02021595 4.043191e-02 9.797840e-01
[19,] 0.03631424 7.262849e-02 9.636858e-01
[20,] 0.02948035 5.896070e-02 9.705197e-01
[21,] 0.02624586 5.249173e-02 9.737541e-01
[22,] 0.03359349 6.718699e-02 9.664065e-01
[23,] 0.04571740 9.143480e-02 9.542826e-01
[24,] 0.07127598 1.425520e-01 9.287240e-01
[25,] 0.20307786 4.061557e-01 7.969221e-01
[26,] 0.31212030 6.242406e-01 6.878797e-01
[27,] 0.29086202 5.817240e-01 7.091380e-01
[28,] 0.25174752 5.034950e-01 7.482525e-01
[29,] 0.21718010 4.343602e-01 7.828199e-01
[30,] 0.17783509 3.556702e-01 8.221649e-01
[31,] 0.25320223 5.064045e-01 7.467978e-01
[32,] 0.27204668 5.440934e-01 7.279533e-01
[33,] 0.30710641 6.142128e-01 6.928936e-01
[34,] 0.26741227 5.348245e-01 7.325877e-01
[35,] 0.24581372 4.916274e-01 7.541863e-01
[36,] 0.23789748 4.757950e-01 7.621025e-01
[37,] 0.31254187 6.250837e-01 6.874581e-01
[38,] 0.31851730 6.370346e-01 6.814827e-01
[39,] 0.30235007 6.047001e-01 6.976499e-01
[40,] 0.35100239 7.020048e-01 6.489976e-01
[41,] 0.38070985 7.614197e-01 6.192902e-01
[42,] 0.60560114 7.887977e-01 3.943989e-01
[43,] 0.93729151 1.254170e-01 6.270849e-02
[44,] 0.99133392 1.733216e-02 8.666078e-03
[45,] 0.99733610 5.327801e-03 2.663901e-03
[46,] 0.99967131 6.573756e-04 3.286878e-04
[47,] 0.99987179 2.564235e-04 1.282118e-04
[48,] 0.99994632 1.073661e-04 5.368304e-05
[49,] 0.99997670 4.660327e-05 2.330164e-05
[50,] 0.99998653 2.694220e-05 1.347110e-05
[51,] 0.99998918 2.164458e-05 1.082229e-05
[52,] 0.99999669 6.620420e-06 3.310210e-06
[53,] 0.99999940 1.200541e-06 6.002706e-07
[54,] 0.99999997 6.710941e-08 3.355470e-08
[55,] 0.99999999 1.972242e-08 9.861210e-09
[56,] 1.00000000 2.984625e-09 1.492313e-09
[57,] 1.00000000 1.822985e-09 9.114926e-10
[58,] 1.00000000 2.139739e-09 1.069869e-09
[59,] 1.00000000 8.224673e-10 4.112337e-10
[60,] 1.00000000 6.917765e-10 3.458882e-10
[61,] 1.00000000 6.647710e-10 3.323855e-10
[62,] 1.00000000 1.485607e-09 7.428033e-10
[63,] 1.00000000 3.653804e-09 1.826902e-09
[64,] 1.00000000 6.769730e-09 3.384865e-09
[65,] 1.00000000 8.254501e-09 4.127250e-09
[66,] 1.00000000 4.423825e-09 2.211913e-09
[67,] 1.00000000 9.564005e-10 4.782003e-10
[68,] 1.00000000 6.181339e-10 3.090669e-10
[69,] 1.00000000 2.771710e-10 1.385855e-10
[70,] 1.00000000 1.060114e-09 5.300570e-10
[71,] 1.00000000 6.232501e-10 3.116250e-10
[72,] 1.00000000 2.357003e-09 1.178501e-09
[73,] 1.00000000 8.544284e-09 4.272142e-09
[74,] 0.99999999 2.446074e-08 1.223037e-08
[75,] 0.99999999 2.796785e-08 1.398393e-08
[76,] 0.99999997 6.841833e-08 3.420917e-08
[77,] 0.99999985 2.938564e-07 1.469282e-07
[78,] 0.99999959 8.144072e-07 4.072036e-07
[79,] 0.99999851 2.982540e-06 1.491270e-06
[80,] 0.99999522 9.565950e-06 4.782975e-06
[81,] 0.99998580 2.840091e-05 1.420045e-05
[82,] 0.99996129 7.742985e-05 3.871493e-05
[83,] 0.99987442 2.511555e-04 1.255777e-04
[84,] 0.99956967 8.606679e-04 4.303339e-04
[85,] 0.99952596 9.480771e-04 4.740386e-04
[86,] 0.99823778 3.524439e-03 1.762219e-03
[87,] 0.99544624 9.107518e-03 4.553759e-03
[88,] 0.99092722 1.814556e-02 9.072782e-03
[89,] 0.98266995 3.466011e-02 1.733005e-02
> postscript(file="/var/www/html/rcomp/tmp/1qvvz1291648471.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$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qvvz1291648471.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$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/3j4u21291648471.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4j4u21291648471.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5j4u21291648471.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
905.283636 754.415913 428.589015 79.728199 -128.720515 -189.297959
7 8 9 10 11 12
-379.642105 -379.288742 -159.643562 -242.696567 -126.388265 -249.925541
13 14 15 16 17 18
-652.928667 -653.350226 -598.897571 -627.665583 -250.109228 -136.452882
19 20 21 22 23 24
-63.880405 -9.955382 -204.181163 91.186216 154.683910 152.463898
25 26 27 28 29 30
-44.346867 -121.132802 83.543128 20.475975 -131.624621 -46.734537
31 32 33 34 35 36
198.294196 212.600670 436.103014 253.143476 67.591223 133.590799
37 38 39 40 41 42
147.311025 184.680355 -112.958851 76.060092 136.462893 196.096525
43 44 45 46 47 48
336.233560 232.897229 258.835481 197.733976 98.717455 146.503937
49 50 51 52 53 54
-72.972093 -94.619120 -208.485274 -22.607780 -7.792707 -132.383830
55 56 57 58 59 60
-244.987331 -214.850249 -320.264892 -520.164647 -316.399456 -371.783609
61 62 63 64 65 66
-581.719849 -518.700743 -474.842731 -442.276594 -259.446389 -249.606268
67 68 69 70 71 72
-223.835966 -85.972951 -80.766706 74.042088 44.107143 97.419422
73 74 75 76 77 78
-8.437475 24.675626 129.349827 329.858729 249.503081 273.768159
79 80 81 82 83 84
298.515713 353.317729 211.528262 298.167324 188.134382 306.494522
85 86 87 88 89 90
272.219798 317.479481 336.733843 259.346267 225.902849 302.083726
91 92 93 94 95 96
387.449508 409.929489 332.981484 309.048478 360.417234 307.853572
97 98 99 100 101 102
263.341991 240.667190 313.724774 393.734558 278.679810 171.665606
103 104 105 106 107 108
19.579902 -92.331857 -155.003975 -179.532821 -153.103987 -81.901537
109 110 111 112 113 114
-104.446515 -163.033356 19.212098 -31.001721 -131.479700 -94.183877
115 116 117 118 119 120
-234.450032 -296.255412 -172.580704 -89.851742 -33.240898 -110.552800
121 122 123 124 125 126
-123.304984 28.917683 84.031743 -35.652143 18.624527 -94.954662
127 128 129 130 131 132
-93.277041 -130.090525 -147.007238 -191.075780 -284.518741 -330.162663
> postscript(file="/var/www/html/rcomp/tmp/6cvtn1291648471.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
0 905.283636 NA
1 754.415913 905.283636
2 428.589015 754.415913
3 79.728199 428.589015
4 -128.720515 79.728199
5 -189.297959 -128.720515
6 -379.642105 -189.297959
7 -379.288742 -379.642105
8 -159.643562 -379.288742
9 -242.696567 -159.643562
10 -126.388265 -242.696567
11 -249.925541 -126.388265
12 -652.928667 -249.925541
13 -653.350226 -652.928667
14 -598.897571 -653.350226
15 -627.665583 -598.897571
16 -250.109228 -627.665583
17 -136.452882 -250.109228
18 -63.880405 -136.452882
19 -9.955382 -63.880405
20 -204.181163 -9.955382
21 91.186216 -204.181163
22 154.683910 91.186216
23 152.463898 154.683910
24 -44.346867 152.463898
25 -121.132802 -44.346867
26 83.543128 -121.132802
27 20.475975 83.543128
28 -131.624621 20.475975
29 -46.734537 -131.624621
30 198.294196 -46.734537
31 212.600670 198.294196
32 436.103014 212.600670
33 253.143476 436.103014
34 67.591223 253.143476
35 133.590799 67.591223
36 147.311025 133.590799
37 184.680355 147.311025
38 -112.958851 184.680355
39 76.060092 -112.958851
40 136.462893 76.060092
41 196.096525 136.462893
42 336.233560 196.096525
43 232.897229 336.233560
44 258.835481 232.897229
45 197.733976 258.835481
46 98.717455 197.733976
47 146.503937 98.717455
48 -72.972093 146.503937
49 -94.619120 -72.972093
50 -208.485274 -94.619120
51 -22.607780 -208.485274
52 -7.792707 -22.607780
53 -132.383830 -7.792707
54 -244.987331 -132.383830
55 -214.850249 -244.987331
56 -320.264892 -214.850249
57 -520.164647 -320.264892
58 -316.399456 -520.164647
59 -371.783609 -316.399456
60 -581.719849 -371.783609
61 -518.700743 -581.719849
62 -474.842731 -518.700743
63 -442.276594 -474.842731
64 -259.446389 -442.276594
65 -249.606268 -259.446389
66 -223.835966 -249.606268
67 -85.972951 -223.835966
68 -80.766706 -85.972951
69 74.042088 -80.766706
70 44.107143 74.042088
71 97.419422 44.107143
72 -8.437475 97.419422
73 24.675626 -8.437475
74 129.349827 24.675626
75 329.858729 129.349827
76 249.503081 329.858729
77 273.768159 249.503081
78 298.515713 273.768159
79 353.317729 298.515713
80 211.528262 353.317729
81 298.167324 211.528262
82 188.134382 298.167324
83 306.494522 188.134382
84 272.219798 306.494522
85 317.479481 272.219798
86 336.733843 317.479481
87 259.346267 336.733843
88 225.902849 259.346267
89 302.083726 225.902849
90 387.449508 302.083726
91 409.929489 387.449508
92 332.981484 409.929489
93 309.048478 332.981484
94 360.417234 309.048478
95 307.853572 360.417234
96 263.341991 307.853572
97 240.667190 263.341991
98 313.724774 240.667190
99 393.734558 313.724774
100 278.679810 393.734558
101 171.665606 278.679810
102 19.579902 171.665606
103 -92.331857 19.579902
104 -155.003975 -92.331857
105 -179.532821 -155.003975
106 -153.103987 -179.532821
107 -81.901537 -153.103987
108 -104.446515 -81.901537
109 -163.033356 -104.446515
110 19.212098 -163.033356
111 -31.001721 19.212098
112 -131.479700 -31.001721
113 -94.183877 -131.479700
114 -234.450032 -94.183877
115 -296.255412 -234.450032
116 -172.580704 -296.255412
117 -89.851742 -172.580704
118 -33.240898 -89.851742
119 -110.552800 -33.240898
120 -123.304984 -110.552800
121 28.917683 -123.304984
122 84.031743 28.917683
123 -35.652143 84.031743
124 18.624527 -35.652143
125 -94.954662 18.624527
126 -93.277041 -94.954662
127 -130.090525 -93.277041
128 -147.007238 -130.090525
129 -191.075780 -147.007238
130 -284.518741 -191.075780
131 -330.162663 -284.518741
132 NA -330.162663
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 754.415913 905.283636
[2,] 428.589015 754.415913
[3,] 79.728199 428.589015
[4,] -128.720515 79.728199
[5,] -189.297959 -128.720515
[6,] -379.642105 -189.297959
[7,] -379.288742 -379.642105
[8,] -159.643562 -379.288742
[9,] -242.696567 -159.643562
[10,] -126.388265 -242.696567
[11,] -249.925541 -126.388265
[12,] -652.928667 -249.925541
[13,] -653.350226 -652.928667
[14,] -598.897571 -653.350226
[15,] -627.665583 -598.897571
[16,] -250.109228 -627.665583
[17,] -136.452882 -250.109228
[18,] -63.880405 -136.452882
[19,] -9.955382 -63.880405
[20,] -204.181163 -9.955382
[21,] 91.186216 -204.181163
[22,] 154.683910 91.186216
[23,] 152.463898 154.683910
[24,] -44.346867 152.463898
[25,] -121.132802 -44.346867
[26,] 83.543128 -121.132802
[27,] 20.475975 83.543128
[28,] -131.624621 20.475975
[29,] -46.734537 -131.624621
[30,] 198.294196 -46.734537
[31,] 212.600670 198.294196
[32,] 436.103014 212.600670
[33,] 253.143476 436.103014
[34,] 67.591223 253.143476
[35,] 133.590799 67.591223
[36,] 147.311025 133.590799
[37,] 184.680355 147.311025
[38,] -112.958851 184.680355
[39,] 76.060092 -112.958851
[40,] 136.462893 76.060092
[41,] 196.096525 136.462893
[42,] 336.233560 196.096525
[43,] 232.897229 336.233560
[44,] 258.835481 232.897229
[45,] 197.733976 258.835481
[46,] 98.717455 197.733976
[47,] 146.503937 98.717455
[48,] -72.972093 146.503937
[49,] -94.619120 -72.972093
[50,] -208.485274 -94.619120
[51,] -22.607780 -208.485274
[52,] -7.792707 -22.607780
[53,] -132.383830 -7.792707
[54,] -244.987331 -132.383830
[55,] -214.850249 -244.987331
[56,] -320.264892 -214.850249
[57,] -520.164647 -320.264892
[58,] -316.399456 -520.164647
[59,] -371.783609 -316.399456
[60,] -581.719849 -371.783609
[61,] -518.700743 -581.719849
[62,] -474.842731 -518.700743
[63,] -442.276594 -474.842731
[64,] -259.446389 -442.276594
[65,] -249.606268 -259.446389
[66,] -223.835966 -249.606268
[67,] -85.972951 -223.835966
[68,] -80.766706 -85.972951
[69,] 74.042088 -80.766706
[70,] 44.107143 74.042088
[71,] 97.419422 44.107143
[72,] -8.437475 97.419422
[73,] 24.675626 -8.437475
[74,] 129.349827 24.675626
[75,] 329.858729 129.349827
[76,] 249.503081 329.858729
[77,] 273.768159 249.503081
[78,] 298.515713 273.768159
[79,] 353.317729 298.515713
[80,] 211.528262 353.317729
[81,] 298.167324 211.528262
[82,] 188.134382 298.167324
[83,] 306.494522 188.134382
[84,] 272.219798 306.494522
[85,] 317.479481 272.219798
[86,] 336.733843 317.479481
[87,] 259.346267 336.733843
[88,] 225.902849 259.346267
[89,] 302.083726 225.902849
[90,] 387.449508 302.083726
[91,] 409.929489 387.449508
[92,] 332.981484 409.929489
[93,] 309.048478 332.981484
[94,] 360.417234 309.048478
[95,] 307.853572 360.417234
[96,] 263.341991 307.853572
[97,] 240.667190 263.341991
[98,] 313.724774 240.667190
[99,] 393.734558 313.724774
[100,] 278.679810 393.734558
[101,] 171.665606 278.679810
[102,] 19.579902 171.665606
[103,] -92.331857 19.579902
[104,] -155.003975 -92.331857
[105,] -179.532821 -155.003975
[106,] -153.103987 -179.532821
[107,] -81.901537 -153.103987
[108,] -104.446515 -81.901537
[109,] -163.033356 -104.446515
[110,] 19.212098 -163.033356
[111,] -31.001721 19.212098
[112,] -131.479700 -31.001721
[113,] -94.183877 -131.479700
[114,] -234.450032 -94.183877
[115,] -296.255412 -234.450032
[116,] -172.580704 -296.255412
[117,] -89.851742 -172.580704
[118,] -33.240898 -89.851742
[119,] -110.552800 -33.240898
[120,] -123.304984 -110.552800
[121,] 28.917683 -123.304984
[122,] 84.031743 28.917683
[123,] -35.652143 84.031743
[124,] 18.624527 -35.652143
[125,] -94.954662 18.624527
[126,] -93.277041 -94.954662
[127,] -130.090525 -93.277041
[128,] -147.007238 -130.090525
[129,] -191.075780 -147.007238
[130,] -284.518741 -191.075780
[131,] -330.162663 -284.518741
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 754.415913 905.283636
2 428.589015 754.415913
3 79.728199 428.589015
4 -128.720515 79.728199
5 -189.297959 -128.720515
6 -379.642105 -189.297959
7 -379.288742 -379.642105
8 -159.643562 -379.288742
9 -242.696567 -159.643562
10 -126.388265 -242.696567
11 -249.925541 -126.388265
12 -652.928667 -249.925541
13 -653.350226 -652.928667
14 -598.897571 -653.350226
15 -627.665583 -598.897571
16 -250.109228 -627.665583
17 -136.452882 -250.109228
18 -63.880405 -136.452882
19 -9.955382 -63.880405
20 -204.181163 -9.955382
21 91.186216 -204.181163
22 154.683910 91.186216
23 152.463898 154.683910
24 -44.346867 152.463898
25 -121.132802 -44.346867
26 83.543128 -121.132802
27 20.475975 83.543128
28 -131.624621 20.475975
29 -46.734537 -131.624621
30 198.294196 -46.734537
31 212.600670 198.294196
32 436.103014 212.600670
33 253.143476 436.103014
34 67.591223 253.143476
35 133.590799 67.591223
36 147.311025 133.590799
37 184.680355 147.311025
38 -112.958851 184.680355
39 76.060092 -112.958851
40 136.462893 76.060092
41 196.096525 136.462893
42 336.233560 196.096525
43 232.897229 336.233560
44 258.835481 232.897229
45 197.733976 258.835481
46 98.717455 197.733976
47 146.503937 98.717455
48 -72.972093 146.503937
49 -94.619120 -72.972093
50 -208.485274 -94.619120
51 -22.607780 -208.485274
52 -7.792707 -22.607780
53 -132.383830 -7.792707
54 -244.987331 -132.383830
55 -214.850249 -244.987331
56 -320.264892 -214.850249
57 -520.164647 -320.264892
58 -316.399456 -520.164647
59 -371.783609 -316.399456
60 -581.719849 -371.783609
61 -518.700743 -581.719849
62 -474.842731 -518.700743
63 -442.276594 -474.842731
64 -259.446389 -442.276594
65 -249.606268 -259.446389
66 -223.835966 -249.606268
67 -85.972951 -223.835966
68 -80.766706 -85.972951
69 74.042088 -80.766706
70 44.107143 74.042088
71 97.419422 44.107143
72 -8.437475 97.419422
73 24.675626 -8.437475
74 129.349827 24.675626
75 329.858729 129.349827
76 249.503081 329.858729
77 273.768159 249.503081
78 298.515713 273.768159
79 353.317729 298.515713
80 211.528262 353.317729
81 298.167324 211.528262
82 188.134382 298.167324
83 306.494522 188.134382
84 272.219798 306.494522
85 317.479481 272.219798
86 336.733843 317.479481
87 259.346267 336.733843
88 225.902849 259.346267
89 302.083726 225.902849
90 387.449508 302.083726
91 409.929489 387.449508
92 332.981484 409.929489
93 309.048478 332.981484
94 360.417234 309.048478
95 307.853572 360.417234
96 263.341991 307.853572
97 240.667190 263.341991
98 313.724774 240.667190
99 393.734558 313.724774
100 278.679810 393.734558
101 171.665606 278.679810
102 19.579902 171.665606
103 -92.331857 19.579902
104 -155.003975 -92.331857
105 -179.532821 -155.003975
106 -153.103987 -179.532821
107 -81.901537 -153.103987
108 -104.446515 -81.901537
109 -163.033356 -104.446515
110 19.212098 -163.033356
111 -31.001721 19.212098
112 -131.479700 -31.001721
113 -94.183877 -131.479700
114 -234.450032 -94.183877
115 -296.255412 -234.450032
116 -172.580704 -296.255412
117 -89.851742 -172.580704
118 -33.240898 -89.851742
119 -110.552800 -33.240898
120 -123.304984 -110.552800
121 28.917683 -123.304984
122 84.031743 28.917683
123 -35.652143 84.031743
124 18.624527 -35.652143
125 -94.954662 18.624527
126 -93.277041 -94.954662
127 -130.090525 -93.277041
128 -147.007238 -130.090525
129 -191.075780 -147.007238
130 -284.518741 -191.075780
131 -330.162663 -284.518741
> 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/7m4s81291648471.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8m4s81291648471.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9m4s81291648471.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10fd9s1291648471.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$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/110wqy1291648471.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/124wpm1291648471.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/13064v1291648471.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/14l7l11291648471.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/157p171291648471.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/16s8ic1291648471.tab")
+ }
>
> try(system("convert tmp/1qvvz1291648471.ps tmp/1qvvz1291648471.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qvvz1291648471.ps tmp/2qvvz1291648471.png",intern=TRUE))
character(0)
> try(system("convert tmp/3j4u21291648471.ps tmp/3j4u21291648471.png",intern=TRUE))
character(0)
> try(system("convert tmp/4j4u21291648471.ps tmp/4j4u21291648471.png",intern=TRUE))
character(0)
> try(system("convert tmp/5j4u21291648471.ps tmp/5j4u21291648471.png",intern=TRUE))
character(0)
> try(system("convert tmp/6cvtn1291648471.ps tmp/6cvtn1291648471.png",intern=TRUE))
character(0)
> try(system("convert tmp/7m4s81291648471.ps tmp/7m4s81291648471.png",intern=TRUE))
character(0)
> try(system("convert tmp/8m4s81291648471.ps tmp/8m4s81291648471.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m4s81291648471.ps tmp/9m4s81291648471.png",intern=TRUE))
character(0)
> try(system("convert tmp/10fd9s1291648471.ps tmp/10fd9s1291648471.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
3.811 1.749 8.575