R version 2.12.0 (2010-10-15)
Copyright (C) 2010 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: i486-pc-linux-gnu (32-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
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+ ,-0.0752062699
+ ,0.0788578228
+ ,0.0271390938
+ ,0.0390499463
+ ,0.0482862048
+ ,0.0379680959
+ ,0.0206244894
+ ,0.0208474516
+ ,-0.0289088246
+ ,0.1009621422
+ ,0.0229004195
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+ ,-0.0057465023
+ ,0.0271390938
+ ,0.0646729207
+ ,-0.0321652782
+ ,0.0549314322
+ ,0.0788578228
+ ,0.0208474516
+ ,-0.1458885691
+ ,0.0424872334
+ ,0.0361686515
+ ,-0.0626807665
+ ,-0.0245446326
+ ,-0.0057465023
+ ,-0.0010757027
+ ,0.0646729207
+ ,-0.0321652782
+ ,0.0549314322
+ ,0.0788578228
+ ,-0.0677816324
+ ,0.0275361898
+ ,0.0424872334
+ ,0.0361686515
+ ,-0.0626807665
+ ,-0.0245446326
+ ,-0.1458885691
+ ,-0.0010757027
+ ,0.0646729207
+ ,-0.0321652782
+ ,0.0549314322
+ ,-0.0098770369
+ ,0.0415469527
+ ,0.0275361898
+ ,0.0424872334
+ ,0.0361686515
+ ,-0.0626807665
+ ,-0.0677816324
+ ,-0.1458885691
+ ,-0.0010757027
+ ,0.0646729207
+ ,-0.0321652782
+ ,0.0501991563
+ ,0.0146867637
+ ,0.0415469527
+ ,0.0275361898
+ ,0.0424872334
+ ,0.0361686515
+ ,-0.0098770369
+ ,-0.0677816324
+ ,-0.1458885691
+ ,-0.0010757027
+ ,0.0646729207
+ ,-0.1271063631
+ ,0.0214691709
+ ,0.0146867637
+ ,0.0415469527
+ ,0.0275361898
+ ,0.0424872334
+ ,0.0501991563
+ ,-0.0098770369
+ ,-0.0677816324
+ ,-0.1458885691
+ ,-0.0010757027
+ ,0.0582277708
+ ,0.0350404489
+ ,0.0214691709
+ ,0.0146867637
+ ,0.0415469527
+ ,0.0275361898
+ ,-0.1271063631
+ ,0.0501991563
+ ,-0.0098770369
+ ,-0.0677816324
+ ,-0.1458885691
+ ,0.0595552648
+ ,0.0131292719
+ ,0.0350404489
+ ,0.0214691709
+ ,0.0146867637
+ ,0.0415469527
+ ,0.0582277708
+ ,-0.1271063631
+ ,0.0501991563
+ ,-0.0098770369
+ ,-0.0677816324
+ ,0.0885041253
+ ,-0.0328847346
+ ,0.0131292719
+ ,0.0350404489
+ ,0.0214691709
+ ,0.0146867637
+ ,0.0595552648
+ ,0.0582277708
+ ,-0.1271063631
+ ,0.0501991563
+ ,-0.0098770369
+ ,0.0004433607
+ ,0.0523775174
+ ,-0.0328847346
+ ,0.0131292719
+ ,0.0350404489
+ ,0.0214691709
+ ,0.0885041253
+ ,0.0595552648
+ ,0.0582277708
+ ,-0.1271063631
+ ,0.0501991563
+ ,0.0379810835
+ ,0.0227588167
+ ,0.0523775174
+ ,-0.0328847346
+ ,0.0131292719
+ ,0.0350404489
+ ,0.0004433607
+ ,0.0885041253
+ ,0.0595552648
+ ,0.0582277708
+ ,-0.1271063631
+ ,0.0681769863
+ ,-0.0162178014
+ ,0.0227588167
+ ,0.0523775174
+ ,-0.0328847346
+ ,0.0131292719
+ ,0.0379810835
+ ,0.0004433607
+ ,0.0885041253
+ ,0.0595552648
+ ,0.0582277708
+ ,-0.0520908486
+ ,-0.0127524528
+ ,-0.0162178014
+ ,0.0227588167
+ ,0.0523775174
+ ,-0.0328847346
+ ,0.0681769863
+ ,0.0379810835
+ ,0.0004433607
+ ,0.0885041253
+ ,0.0595552648
+ ,0.0666010623
+ ,-0.0822608913
+ ,-0.0127524528
+ ,-0.0162178014
+ ,0.0227588167
+ ,0.0523775174
+ ,-0.0520908486
+ ,0.0681769863
+ ,0.0379810835
+ ,0.0004433607
+ ,0.0885041253
+ ,0.0677173945
+ ,0.0221140649
+ ,-0.0822608913
+ ,-0.0127524528
+ ,-0.0162178014
+ ,0.0227588167
+ ,0.0666010623
+ ,-0.0520908486
+ ,0.0681769863
+ ,0.0379810835
+ ,0.0004433607
+ ,0.1251127483
+ ,0.0317810643
+ ,0.0221140649
+ ,-0.0822608913
+ ,-0.0127524528
+ ,-0.0162178014
+ ,0.0677173945
+ ,0.0666010623
+ ,-0.0520908486
+ ,0.0681769863
+ ,0.0379810835
+ ,-0.0218349286
+ ,-0.0773298837
+ ,0.0317810643
+ ,0.0221140649
+ ,-0.0822608913
+ ,-0.0127524528
+ ,0.1251127483
+ ,0.0677173945
+ ,0.0666010623
+ ,-0.0520908486
+ ,0.0681769863
+ ,0.0178312442
+ ,0.0027974224
+ ,-0.0773298837
+ ,0.0317810643
+ ,0.0221140649
+ ,-0.0822608913
+ ,-0.0218349286
+ ,0.1251127483
+ ,0.0677173945
+ ,0.0666010623
+ ,-0.0520908486
+ ,0.0199663138
+ ,-0.0684060589
+ ,0.0027974224
+ ,-0.0773298837
+ ,0.0317810643
+ ,0.0221140649
+ ,0.0178312442
+ ,-0.0218349286
+ ,0.1251127483
+ ,0.0677173945
+ ,0.0666010623
+ ,0.088436301
+ ,-0.0326538799
+ ,-0.0684060589
+ ,0.0027974224
+ ,-0.0773298837
+ ,0.0317810643
+ ,0.0199663138
+ ,0.0178312442
+ ,-0.0218349286
+ ,0.1251127483
+ ,0.0677173945
+ ,0.0646054028
+ ,-0.0125972204
+ ,-0.0326538799
+ ,-0.0684060589
+ ,0.0027974224
+ ,-0.0773298837
+ ,0.088436301
+ ,0.0199663138
+ ,0.0178312442
+ ,-0.0218349286
+ ,0.1251127483
+ ,0.1233912353
+ ,0.048660559
+ ,-0.0125972204
+ ,-0.0326538799
+ ,-0.0684060589
+ ,0.0027974224
+ ,0.0646054028
+ ,0.088436301
+ ,0.0199663138
+ ,0.0178312442
+ ,-0.0218349286
+ ,0.0673308704
+ ,-0.0147704378
+ ,0.048660559
+ ,-0.0125972204
+ ,-0.0326538799
+ ,-0.0684060589
+ ,0.1233912353
+ ,0.0646054028
+ ,0.088436301
+ ,0.0199663138
+ ,0.0178312442
+ ,0.0232412696
+ ,-0.0812692141
+ ,-0.0147704378
+ ,0.048660559
+ ,-0.0125972204
+ ,-0.0326538799
+ ,0.0673308704
+ ,0.1233912353
+ ,0.0646054028
+ ,0.088436301
+ ,0.0199663138
+ ,-0.1570633927
+ ,-0.1445904237
+ ,-0.0812692141
+ ,-0.0147704378
+ ,0.048660559
+ ,-0.0125972204
+ ,0.0232412696
+ ,0.0673308704
+ ,0.1233912353
+ ,0.0646054028
+ ,0.088436301
+ ,-0.134923147
+ ,0.0047470083
+ ,-0.1445904237
+ ,-0.0812692141
+ ,-0.0147704378
+ ,0.048660559
+ ,-0.1570633927
+ ,0.0232412696
+ ,0.0673308704
+ ,0.1233912353
+ ,0.0646054028
+ ,-0.2920959272
+ ,-0.0281878491
+ ,0.0047470083
+ ,-0.1445904237
+ ,-0.0812692141
+ ,-0.0147704378
+ ,-0.134923147
+ ,-0.1570633927
+ ,0.0232412696
+ ,0.0673308704
+ ,0.1233912353
+ ,-0.3111517877
+ ,-0.2985135366
+ ,-0.0281878491
+ ,0.0047470083
+ ,-0.1445904237
+ ,-0.0812692141
+ ,-0.2920959272
+ ,-0.134923147
+ ,-0.1570633927
+ ,0.0232412696
+ ,0.0673308704
+ ,-0.2363887781
+ ,-0.0871197547
+ ,-0.2985135366
+ ,-0.0281878491
+ ,0.0047470083
+ ,-0.1445904237
+ ,-0.3111517877
+ ,-0.2920959272
+ ,-0.134923147
+ ,-0.1570633927
+ ,0.0232412696
+ ,0.0334524402
+ ,-0.0782493685
+ ,-0.0871197547
+ ,-0.2985135366
+ ,-0.0281878491
+ ,0.0047470083
+ ,-0.2363887781
+ ,-0.3111517877
+ ,-0.2920959272
+ ,-0.134923147
+ ,-0.1570633927)
+ ,dim=c(11
+ ,103)
+ ,dimnames=list(c('(1-B)lnYt'
+ ,'(1-B)lnX_[t-1]'
+ ,'(1-B)lnX_[t-2]'
+ ,'(1-B)lnX_[t-3]'
+ ,'(1-B)lnX_[t-4]'
+ ,'(1-B)lnX_[t-5]'
+ ,'(1-B)lnY_[t-1]'
+ ,'(1-B)lnY_[t-2]'
+ ,'(1-B)lnY_[t-3]'
+ ,'(1-B)lnY_[t-4]'
+ ,'(1-B)lnY_[t-5]')
+ ,1:103))
> y <- array(NA,dim=c(11,103),dimnames=list(c('(1-B)lnYt','(1-B)lnX_[t-1]','(1-B)lnX_[t-2]','(1-B)lnX_[t-3]','(1-B)lnX_[t-4]','(1-B)lnX_[t-5]','(1-B)lnY_[t-1]','(1-B)lnY_[t-2]','(1-B)lnY_[t-3]','(1-B)lnY_[t-4]','(1-B)lnY_[t-5]'),1:103))
> 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 = 'Do not include Seasonal Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
(1-B)lnYt (1-B)lnX_[t-1] (1-B)lnX_[t-2] (1-B)lnX_[t-3] (1-B)lnX_[t-4]
1 -0.0326433382 0.011700669 -0.006696904 0.040701306 -0.012866577
2 0.0440720340 0.039110383 0.011700669 -0.006696904 0.040701306
3 0.0882361169 0.042355037 0.039110383 0.011700669 -0.006696904
4 -0.0355066885 -0.035632700 0.042355037 0.039110383 0.011700669
5 0.0278273388 0.004227877 -0.035632700 0.042355037 0.039110383
6 -0.2004308914 0.021032889 0.004227877 -0.035632700 0.042355037
7 -0.0263424279 -0.031774003 0.021032889 0.004227877 -0.035632700
8 0.0655051718 -0.008712895 -0.031774003 0.021032889 0.004227877
9 -0.0709357514 0.002824837 -0.008712895 -0.031774003 0.021032889
10 -0.0129185590 -0.055728098 0.002824837 -0.008712895 -0.031774003
11 0.1183957540 0.005554260 -0.055728098 0.002824837 -0.008712895
12 -0.0330932173 -0.019182734 0.005554260 -0.055728098 0.002824837
13 -0.0860908693 0.017883170 -0.019182734 0.005554260 -0.055728098
14 0.0210698391 0.024225659 0.017883170 -0.019182734 0.005554260
15 0.0149456700 -0.007713988 0.024225659 0.017883170 -0.019182734
16 -0.1900347570 -0.104956121 -0.007713988 0.024225659 0.017883170
17 -0.1242436027 0.014193720 -0.104956121 -0.007713988 0.024225659
18 0.0062305498 0.006850970 0.014193720 -0.104956121 -0.007713988
19 0.0255507001 -0.008032748 0.006850970 0.014193720 -0.104956121
20 0.0268806628 0.030501844 -0.008032748 0.006850970 0.014193720
21 0.1773155966 0.009377819 0.030501844 -0.008032748 0.006850970
22 0.0660542373 -0.003714262 0.009377819 0.030501844 -0.008032748
23 -0.0139591255 0.027401938 -0.003714262 0.009377819 0.030501844
24 -0.0373949697 -0.004373458 0.027401938 -0.003714262 0.009377819
25 0.0366137196 -0.091254018 -0.004373458 0.027401938 -0.003714262
26 0.0193504490 -0.103089807 -0.091254018 -0.004373458 0.027401938
27 0.0837801497 -0.039842170 -0.103089807 -0.091254018 -0.004373458
28 -0.0369814175 -0.069707429 -0.039842170 -0.103089807 -0.091254018
29 -0.1113117010 -0.062581883 -0.069707429 -0.039842170 -0.103089807
30 0.1156912701 0.050989201 -0.062581883 -0.069707429 -0.039842170
31 0.0961044085 -0.023100223 0.050989201 -0.062581883 -0.069707429
32 0.0671607829 -0.024637361 -0.023100223 0.050989201 -0.062581883
33 -0.0791409595 -0.097531660 -0.024637361 -0.023100223 0.050989201
34 -0.1831923744 -0.076864778 -0.097531660 -0.024637361 -0.023100223
35 0.0242315729 0.114387377 -0.076864778 -0.097531660 -0.024637361
36 0.0682600023 0.041145272 0.114387377 -0.076864778 -0.097531660
37 0.0345855796 0.025447744 0.041145272 0.114387377 -0.076864778
38 0.0463590447 0.005054385 0.025447744 0.041145272 0.114387377
39 -0.0931151599 0.045559738 0.005054385 0.025447744 0.041145272
40 0.0760673553 0.015437538 0.045559738 0.005054385 0.025447744
41 -0.0110651198 0.013003344 0.015437538 0.045559738 0.005054385
42 0.0284509336 0.024995248 0.013003344 0.015437538 0.045559738
43 0.0368261882 0.010138994 0.024995248 0.013003344 0.015437538
44 -0.0058804482 0.067637206 0.010138994 0.024995248 0.013003344
45 0.0680750192 0.037497948 0.067637206 0.010138994 0.024995248
46 0.0157914001 -0.013038220 0.037497948 0.067637206 0.010138994
47 0.1121766425 0.026292883 -0.013038220 0.037497948 0.067637206
48 -0.0437664455 -0.026724755 0.026292883 -0.013038220 0.037497948
49 0.0603266530 0.019341562 -0.026724755 0.026292883 -0.013038220
50 0.1028673441 -0.002680174 0.019341562 -0.026724755 0.026292883
51 0.0259509727 0.020134569 -0.002680174 0.019341562 -0.026724755
52 0.1348695746 0.057486644 0.020134569 -0.002680174 0.019341562
53 -0.0967153180 0.041143075 0.057486644 0.020134569 -0.002680174
54 -0.1035936648 0.033006744 0.041143075 0.057486644 0.020134569
55 0.0950389075 0.025013824 0.033006744 0.041143075 0.057486644
56 0.0359719068 0.020467268 0.025013824 0.033006744 0.041143075
57 0.1520609453 0.032573978 0.020467268 0.025013824 0.033006744
58 -0.0022505636 0.008288662 0.032573978 0.020467268 0.025013824
59 -0.0277954311 0.004205271 0.008288662 0.032573978 0.020467268
60 0.0653827593 -0.018785228 0.004205271 0.008288662 0.032573978
61 0.0443888626 0.011707397 -0.018785228 0.004205271 0.008288662
62 0.1010961169 0.020572702 0.011707397 -0.018785228 0.004205271
63 -0.0029750276 0.029748955 0.020572702 0.011707397 -0.018785228
64 -0.0752062699 0.006075771 0.029748955 0.020572702 0.011707397
65 -0.0525843352 0.005571859 0.006075771 0.029748955 0.020572702
66 0.0229004195 0.020624489 0.005571859 0.006075771 0.029748955
67 0.1009621422 0.037968096 0.020624489 0.005571859 0.006075771
68 -0.0289088246 0.048286205 0.037968096 0.020624489 0.005571859
69 0.0208474516 0.039049946 0.048286205 0.037968096 0.020624489
70 0.0788578228 0.027139094 0.039049946 0.048286205 0.037968096
71 0.0549314322 -0.005746502 0.027139094 0.039049946 0.048286205
72 -0.0321652782 -0.024544633 -0.005746502 0.027139094 0.039049946
73 0.0646729207 -0.062680766 -0.024544633 -0.005746502 0.027139094
74 -0.0010757027 0.036168652 -0.062680766 -0.024544633 -0.005746502
75 -0.1458885691 0.042487233 0.036168652 -0.062680766 -0.024544633
76 -0.0677816324 0.027536190 0.042487233 0.036168652 -0.062680766
77 -0.0098770369 0.041546953 0.027536190 0.042487233 0.036168652
78 0.0501991563 0.014686764 0.041546953 0.027536190 0.042487233
79 -0.1271063631 0.021469171 0.014686764 0.041546953 0.027536190
80 0.0582277708 0.035040449 0.021469171 0.014686764 0.041546953
81 0.0595552648 0.013129272 0.035040449 0.021469171 0.014686764
82 0.0885041253 -0.032884735 0.013129272 0.035040449 0.021469171
83 0.0004433607 0.052377517 -0.032884735 0.013129272 0.035040449
84 0.0379810835 0.022758817 0.052377517 -0.032884735 0.013129272
85 0.0681769863 -0.016217801 0.022758817 0.052377517 -0.032884735
86 -0.0520908486 -0.012752453 -0.016217801 0.022758817 0.052377517
87 0.0666010623 -0.082260891 -0.012752453 -0.016217801 0.022758817
88 0.0677173945 0.022114065 -0.082260891 -0.012752453 -0.016217801
89 0.1251127483 0.031781064 0.022114065 -0.082260891 -0.012752453
90 -0.0218349286 -0.077329884 0.031781064 0.022114065 -0.082260891
91 0.0178312442 0.002797422 -0.077329884 0.031781064 0.022114065
92 0.0199663138 -0.068406059 0.002797422 -0.077329884 0.031781064
93 0.0884363010 -0.032653880 -0.068406059 0.002797422 -0.077329884
94 0.0646054028 -0.012597220 -0.032653880 -0.068406059 0.002797422
95 0.1233912353 0.048660559 -0.012597220 -0.032653880 -0.068406059
96 0.0673308704 -0.014770438 0.048660559 -0.012597220 -0.032653880
97 0.0232412696 -0.081269214 -0.014770438 0.048660559 -0.012597220
98 -0.1570633927 -0.144590424 -0.081269214 -0.014770438 0.048660559
99 -0.1349231470 0.004747008 -0.144590424 -0.081269214 -0.014770438
100 -0.2920959272 -0.028187849 0.004747008 -0.144590424 -0.081269214
101 -0.3111517877 -0.298513537 -0.028187849 0.004747008 -0.144590424
102 -0.2363887781 -0.087119755 -0.298513537 -0.028187849 0.004747008
103 0.0334524402 -0.078249368 -0.087119755 -0.298513537 -0.028187849
(1-B)lnX_[t-5] (1-B)lnY_[t-1] (1-B)lnY_[t-2] (1-B)lnY_[t-3] (1-B)lnY_[t-4]
1 -0.077800389 0.0173917427 0.2208242615 -0.1696576949 -0.0125923592
2 -0.012866577 -0.0326433382 0.0173917427 0.2208242615 -0.1696576949
3 0.040701306 0.0440720340 -0.0326433382 0.0173917427 0.2208242615
4 -0.006696904 0.0882361169 0.0440720340 -0.0326433382 0.0173917427
5 0.011700669 -0.0355066885 0.0882361169 0.0440720340 -0.0326433382
6 0.039110383 0.0278273388 -0.0355066885 0.0882361169 0.0440720340
7 0.042355037 -0.2004308914 0.0278273388 -0.0355066885 0.0882361169
8 -0.035632700 -0.0263424279 -0.2004308914 0.0278273388 -0.0355066885
9 0.004227877 0.0655051718 -0.0263424279 -0.2004308914 0.0278273388
10 0.021032889 -0.0709357514 0.0655051718 -0.0263424279 -0.2004308914
11 -0.031774003 -0.0129185590 -0.0709357514 0.0655051718 -0.0263424279
12 -0.008712895 0.1183957540 -0.0129185590 -0.0709357514 0.0655051718
13 0.002824837 -0.0330932173 0.1183957540 -0.0129185590 -0.0709357514
14 -0.055728098 -0.0860908693 -0.0330932173 0.1183957540 -0.0129185590
15 0.005554260 0.0210698391 -0.0860908693 -0.0330932173 0.1183957540
16 -0.019182734 0.0149456700 0.0210698391 -0.0860908693 -0.0330932173
17 0.017883170 -0.1900347570 0.0149456700 0.0210698391 -0.0860908693
18 0.024225659 -0.1242436027 -0.1900347570 0.0149456700 0.0210698391
19 -0.007713988 0.0062305498 -0.1242436027 -0.1900347570 0.0149456700
20 -0.104956121 0.0255507001 0.0062305498 -0.1242436027 -0.1900347570
21 0.014193720 0.0268806628 0.0255507001 0.0062305498 -0.1242436027
22 0.006850970 0.1773155966 0.0268806628 0.0255507001 0.0062305498
23 -0.008032748 0.0660542373 0.1773155966 0.0268806628 0.0255507001
24 0.030501844 -0.0139591255 0.0660542373 0.1773155966 0.0268806628
25 0.009377819 -0.0373949697 -0.0139591255 0.0660542373 0.1773155966
26 -0.003714262 0.0366137196 -0.0373949697 -0.0139591255 0.0660542373
27 0.027401938 0.0193504490 0.0366137196 -0.0373949697 -0.0139591255
28 -0.004373458 0.0837801497 0.0193504490 0.0366137196 -0.0373949697
29 -0.091254018 -0.0369814175 0.0837801497 0.0193504490 0.0366137196
30 -0.103089807 -0.1113117010 -0.0369814175 0.0837801497 0.0193504490
31 -0.039842170 0.1156912701 -0.1113117010 -0.0369814175 0.0837801497
32 -0.069707429 0.0961044085 0.1156912701 -0.1113117010 -0.0369814175
33 -0.062581883 0.0671607829 0.0961044085 0.1156912701 -0.1113117010
34 0.050989201 -0.0791409595 0.0671607829 0.0961044085 0.1156912701
35 -0.023100223 -0.1831923744 -0.0791409595 0.0671607829 0.0961044085
36 -0.024637361 0.0242315729 -0.1831923744 -0.0791409595 0.0671607829
37 -0.097531660 0.0682600023 0.0242315729 -0.1831923744 -0.0791409595
38 -0.076864778 0.0345855796 0.0682600023 0.0242315729 -0.1831923744
39 0.114387377 0.0463590447 0.0345855796 0.0682600023 0.0242315729
40 0.041145272 -0.0931151599 0.0463590447 0.0345855796 0.0682600023
41 0.025447744 0.0760673553 -0.0931151599 0.0463590447 0.0345855796
42 0.005054385 -0.0110651198 0.0760673553 -0.0931151599 0.0463590447
43 0.045559738 0.0284509336 -0.0110651198 0.0760673553 -0.0931151599
44 0.015437538 0.0368261882 0.0284509336 -0.0110651198 0.0760673553
45 0.013003344 -0.0058804482 0.0368261882 0.0284509336 -0.0110651198
46 0.024995248 0.0680750192 -0.0058804482 0.0368261882 0.0284509336
47 0.010138994 0.0157914001 0.0680750192 -0.0058804482 0.0368261882
48 0.067637206 0.1121766425 0.0157914001 0.0680750192 -0.0058804482
49 0.037497948 -0.0437664455 0.1121766425 0.0157914001 0.0680750192
50 -0.013038220 0.0603266530 -0.0437664455 0.1121766425 0.0157914001
51 0.026292883 0.1028673441 0.0603266530 -0.0437664455 0.1121766425
52 -0.026724755 0.0259509727 0.1028673441 0.0603266530 -0.0437664455
53 0.019341562 0.1348695746 0.0259509727 0.1028673441 0.0603266530
54 -0.002680174 -0.0967153180 0.1348695746 0.0259509727 0.1028673441
55 0.020134569 -0.1035936648 -0.0967153180 0.1348695746 0.0259509727
56 0.057486644 0.0950389075 -0.1035936648 -0.0967153180 0.1348695746
57 0.041143075 0.0359719068 0.0950389075 -0.1035936648 -0.0967153180
58 0.033006744 0.1520609453 0.0359719068 0.0950389075 -0.1035936648
59 0.025013824 -0.0022505636 0.1520609453 0.0359719068 0.0950389075
60 0.020467268 -0.0277954311 -0.0022505636 0.1520609453 0.0359719068
61 0.032573978 0.0653827593 -0.0277954311 -0.0022505636 0.1520609453
62 0.008288662 0.0443888626 0.0653827593 -0.0277954311 -0.0022505636
63 0.004205271 0.1010961169 0.0443888626 0.0653827593 -0.0277954311
64 -0.018785228 -0.0029750276 0.1010961169 0.0443888626 0.0653827593
65 0.011707397 -0.0752062699 -0.0029750276 0.1010961169 0.0443888626
66 0.020572702 -0.0525843352 -0.0752062699 -0.0029750276 0.1010961169
67 0.029748955 0.0229004195 -0.0525843352 -0.0752062699 -0.0029750276
68 0.006075771 0.1009621422 0.0229004195 -0.0525843352 -0.0752062699
69 0.005571859 -0.0289088246 0.1009621422 0.0229004195 -0.0525843352
70 0.020624489 0.0208474516 -0.0289088246 0.1009621422 0.0229004195
71 0.037968096 0.0788578228 0.0208474516 -0.0289088246 0.1009621422
72 0.048286205 0.0549314322 0.0788578228 0.0208474516 -0.0289088246
73 0.039049946 -0.0321652782 0.0549314322 0.0788578228 0.0208474516
74 0.027139094 0.0646729207 -0.0321652782 0.0549314322 0.0788578228
75 -0.005746502 -0.0010757027 0.0646729207 -0.0321652782 0.0549314322
76 -0.024544633 -0.1458885691 -0.0010757027 0.0646729207 -0.0321652782
77 -0.062680766 -0.0677816324 -0.1458885691 -0.0010757027 0.0646729207
78 0.036168652 -0.0098770369 -0.0677816324 -0.1458885691 -0.0010757027
79 0.042487233 0.0501991563 -0.0098770369 -0.0677816324 -0.1458885691
80 0.027536190 -0.1271063631 0.0501991563 -0.0098770369 -0.0677816324
81 0.041546953 0.0582277708 -0.1271063631 0.0501991563 -0.0098770369
82 0.014686764 0.0595552648 0.0582277708 -0.1271063631 0.0501991563
83 0.021469171 0.0885041253 0.0595552648 0.0582277708 -0.1271063631
84 0.035040449 0.0004433607 0.0885041253 0.0595552648 0.0582277708
85 0.013129272 0.0379810835 0.0004433607 0.0885041253 0.0595552648
86 -0.032884735 0.0681769863 0.0379810835 0.0004433607 0.0885041253
87 0.052377517 -0.0520908486 0.0681769863 0.0379810835 0.0004433607
88 0.022758817 0.0666010623 -0.0520908486 0.0681769863 0.0379810835
89 -0.016217801 0.0677173945 0.0666010623 -0.0520908486 0.0681769863
90 -0.012752453 0.1251127483 0.0677173945 0.0666010623 -0.0520908486
91 -0.082260891 -0.0218349286 0.1251127483 0.0677173945 0.0666010623
92 0.022114065 0.0178312442 -0.0218349286 0.1251127483 0.0677173945
93 0.031781064 0.0199663138 0.0178312442 -0.0218349286 0.1251127483
94 -0.077329884 0.0884363010 0.0199663138 0.0178312442 -0.0218349286
95 0.002797422 0.0646054028 0.0884363010 0.0199663138 0.0178312442
96 -0.068406059 0.1233912353 0.0646054028 0.0884363010 0.0199663138
97 -0.032653880 0.0673308704 0.1233912353 0.0646054028 0.0884363010
98 -0.012597220 0.0232412696 0.0673308704 0.1233912353 0.0646054028
99 0.048660559 -0.1570633927 0.0232412696 0.0673308704 0.1233912353
100 -0.014770438 -0.1349231470 -0.1570633927 0.0232412696 0.0673308704
101 -0.081269214 -0.2920959272 -0.1349231470 -0.1570633927 0.0232412696
102 -0.144590424 -0.3111517877 -0.2920959272 -0.1349231470 -0.1570633927
103 0.004747008 -0.2363887781 -0.3111517877 -0.2920959272 -0.1349231470
(1-B)lnY_[t-5]
1 0.0869788752
2 -0.0125923592
3 -0.1696576949
4 0.2208242615
5 0.0173917427
6 -0.0326433382
7 0.0440720340
8 0.0882361169
9 -0.0355066885
10 0.0278273388
11 -0.2004308914
12 -0.0263424279
13 0.0655051718
14 -0.0709357514
15 -0.0129185590
16 0.1183957540
17 -0.0330932173
18 -0.0860908693
19 0.0210698391
20 0.0149456700
21 -0.1900347570
22 -0.1242436027
23 0.0062305498
24 0.0255507001
25 0.0268806628
26 0.1773155966
27 0.0660542373
28 -0.0139591255
29 -0.0373949697
30 0.0366137196
31 0.0193504490
32 0.0837801497
33 -0.0369814175
34 -0.1113117010
35 0.1156912701
36 0.0961044085
37 0.0671607829
38 -0.0791409595
39 -0.1831923744
40 0.0242315729
41 0.0682600023
42 0.0345855796
43 0.0463590447
44 -0.0931151599
45 0.0760673553
46 -0.0110651198
47 0.0284509336
48 0.0368261882
49 -0.0058804482
50 0.0680750192
51 0.0157914001
52 0.1121766425
53 -0.0437664455
54 0.0603266530
55 0.1028673441
56 0.0259509727
57 0.1348695746
58 -0.0967153180
59 -0.1035936648
60 0.0950389075
61 0.0359719068
62 0.1520609453
63 -0.0022505636
64 -0.0277954311
65 0.0653827593
66 0.0443888626
67 0.1010961169
68 -0.0029750276
69 -0.0752062699
70 -0.0525843352
71 0.0229004195
72 0.1009621422
73 -0.0289088246
74 0.0208474516
75 0.0788578228
76 0.0549314322
77 -0.0321652782
78 0.0646729207
79 -0.0010757027
80 -0.1458885691
81 -0.0677816324
82 -0.0098770369
83 0.0501991563
84 -0.1271063631
85 0.0582277708
86 0.0595552648
87 0.0885041253
88 0.0004433607
89 0.0379810835
90 0.0681769863
91 -0.0520908486
92 0.0666010623
93 0.0677173945
94 0.1251127483
95 -0.0218349286
96 0.0178312442
97 0.0199663138
98 0.0884363010
99 0.0646054028
100 0.1233912353
101 0.0673308704
102 0.0232412696
103 -0.1570633927
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) `(1-B)lnX_[t-1]` `(1-B)lnX_[t-2]` `(1-B)lnX_[t-3]`
0.0066879 0.5643814 0.1891025 -0.0904227
`(1-B)lnX_[t-4]` `(1-B)lnX_[t-5]` `(1-B)lnY_[t-1]` `(1-B)lnY_[t-2]`
0.1527126 -0.0570342 0.2584084 -0.0554449
`(1-B)lnY_[t-3]` `(1-B)lnY_[t-4]` `(1-B)lnY_[t-5]`
-0.0247154 -0.0114069 0.0002298
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-0.25781 -0.04914 0.00826 0.06061 0.15185
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.0066879 0.0085952 0.778 0.43851
`(1-B)lnX_[t-1]` 0.5643814 0.1730120 3.262 0.00155 **
`(1-B)lnX_[t-2]` 0.1891025 0.1906714 0.992 0.32391
`(1-B)lnX_[t-3]` -0.0904227 0.1887380 -0.479 0.63301
`(1-B)lnX_[t-4]` 0.1527126 0.2272042 0.672 0.50318
`(1-B)lnX_[t-5]` -0.0570342 0.2198389 -0.259 0.79588
`(1-B)lnY_[t-1]` 0.2584084 0.1056818 2.445 0.01638 *
`(1-B)lnY_[t-2]` -0.0554449 0.1054964 -0.526 0.60046
`(1-B)lnY_[t-3]` -0.0247154 0.1025691 -0.241 0.81012
`(1-B)lnY_[t-4]` -0.0114069 0.1089394 -0.105 0.91684
`(1-B)lnY_[t-5]` 0.0002298 0.1056711 0.002 0.99827
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.08343 on 92 degrees of freedom
Multiple R-squared: 0.2521, Adjusted R-squared: 0.1708
F-statistic: 3.101 on 10 and 92 DF, p-value: 0.00191
> 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,] 0.5443729 0.9112542 0.4556271
[2,] 0.4361077 0.8722155 0.5638923
[3,] 0.6750387 0.6499226 0.3249613
[4,] 0.5585266 0.8829469 0.4414734
[5,] 0.5018396 0.9963208 0.4981604
[6,] 0.4875606 0.9751211 0.5124394
[7,] 0.4056201 0.8112402 0.5943799
[8,] 0.3972432 0.7944864 0.6027568
[9,] 0.3131578 0.6263156 0.6868422
[10,] 0.2795481 0.5590962 0.7204519
[11,] 0.2079662 0.4159324 0.7920338
[12,] 0.2234061 0.4468122 0.7765939
[13,] 0.4649389 0.9298779 0.5350611
[14,] 0.6400453 0.7199093 0.3599547
[15,] 0.6494164 0.7011672 0.3505836
[16,] 0.6824601 0.6350798 0.3175399
[17,] 0.7535271 0.4929458 0.2464729
[18,] 0.7307613 0.5384773 0.2692387
[19,] 0.6934235 0.6131531 0.3065765
[20,] 0.6445985 0.7108031 0.3554015
[21,] 0.6813420 0.6373161 0.3186580
[22,] 0.6196507 0.7606986 0.3803493
[23,] 0.5614937 0.8770126 0.4385063
[24,] 0.5183538 0.9632925 0.4816462
[25,] 0.4578258 0.9156516 0.5421742
[26,] 0.5064117 0.9871766 0.4935883
[27,] 0.5388226 0.9223548 0.4611774
[28,] 0.4886472 0.9772945 0.5113528
[29,] 0.4392639 0.8785278 0.5607361
[30,] 0.3837525 0.7675050 0.6162475
[31,] 0.3394560 0.6789120 0.6605440
[32,] 0.2979060 0.5958119 0.7020940
[33,] 0.2445095 0.4890191 0.7554905
[34,] 0.2652952 0.5305905 0.7347048
[35,] 0.2470142 0.4940284 0.7529858
[36,] 0.2394710 0.4789420 0.7605290
[37,] 0.2301458 0.4602916 0.7698542
[38,] 0.1862520 0.3725039 0.8137480
[39,] 0.2017550 0.4035101 0.7982450
[40,] 0.3252759 0.6505518 0.6747241
[41,] 0.3413319 0.6826638 0.6586681
[42,] 0.3737930 0.7475860 0.6262070
[43,] 0.3309628 0.6619255 0.6690372
[44,] 0.4077798 0.8155597 0.5922202
[45,] 0.3921754 0.7843507 0.6078246
[46,] 0.3706634 0.7413268 0.6293366
[47,] 0.4021101 0.8042202 0.5978899
[48,] 0.3472254 0.6944507 0.6527746
[49,] 0.3905634 0.7811268 0.6094366
[50,] 0.3508342 0.7016683 0.6491658
[51,] 0.3623763 0.7247525 0.6376237
[52,] 0.3168904 0.6337808 0.6831096
[53,] 0.2674970 0.5349940 0.7325030
[54,] 0.3020787 0.6041573 0.6979213
[55,] 0.3245976 0.6491951 0.6754024
[56,] 0.2690496 0.5380993 0.7309504
[57,] 0.2270908 0.4541816 0.7729092
[58,] 0.1831576 0.3663152 0.8168424
[59,] 0.1417667 0.2835334 0.8582333
[60,] 0.1425031 0.2850063 0.8574969
[61,] 0.1178097 0.2356193 0.8821903
[62,] 0.2358406 0.4716812 0.7641594
[63,] 0.2255501 0.4511001 0.7744499
[64,] 0.1883442 0.3766883 0.8116558
[65,] 0.1605620 0.3211241 0.8394380
[66,] 0.4392704 0.8785409 0.5607296
[67,] 0.3748643 0.7497285 0.6251357
[68,] 0.2933785 0.5867571 0.7066215
[69,] 0.2664324 0.5328649 0.7335676
[70,] 0.3696728 0.7393457 0.6303272
[71,] 0.3734103 0.7468205 0.6265897
[72,] 0.3285821 0.6571642 0.6714179
[73,] 0.7123279 0.5753442 0.2876721
[74,] 0.6051407 0.7897185 0.3948593
[75,] 0.4887395 0.9774790 0.5112605
[76,] 0.5985880 0.8028241 0.4014120
> postscript(file="/var/www/rcomp/tmp/1qcyi1291887467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/2qcyi1291887467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/3qcyi1291887467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/41mxl1291887467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/51mxl1291887467.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 = 103
Frequency = 1
1 2 3 4 5
-0.0400679410 0.0184683021 0.0444392530 -0.0497424806 0.0387964310
6 7 8 9 10
-0.2337168788 0.0426191914 0.0649238030 -0.1064300476 0.0355938567
11 12 13 14 15
0.1196550236 -0.0682831511 -0.0760979821 0.0147733718 0.0032111763
16 17 18 19 20
-0.1428894996 -0.0729903013 0.0082599417 0.0259425302 -0.0145360060
21 22 23 24 25
0.1518548894 0.0204663843 -0.0459613393 -0.0348719885 0.0983729320
26 27 28 29 30
0.0721420100 0.1089833343 -0.0125240011 -0.0474558610 0.1149584182
31 32 33 34 35
0.0594956047 0.0673260574 -0.0499978928 -0.0959527967 0.0068267636
36 37 38 39 40
0.0056276183 0.0005322508 0.0072493419 -0.1319867014 0.0792288094
41 42 43 44 45
-0.0465066061 0.0052201807 0.0139478585 -0.0588271592 0.0293957856
46 47 48 49 50
-0.0013157252 0.0867169267 -0.0698960595 0.0729810706 0.0717805561
51 52 53 54 55
-0.0073067109 0.0871729876 -0.1643334282 -0.1004490102 0.0890945086
56 57 58 59 60
-0.0181778108 0.1149657132 -0.0559728751 -0.0261681460 0.0766485209
61 62 63 64 65
0.0188522155 0.0701224719 -0.0485393891 -0.0837245513 -0.0410909451
66 67 68 69 70
0.0111863484 0.0594691602 -0.0956444666 -0.0033576611 0.0449915381
71 72 73 74 75
0.0258874984 -0.0343296364 0.1091162912 -0.0323604695 -0.1819650628
76 77 78 79 80
-0.0477441173 -0.0403319129 0.0205914182 -0.1635721455 0.0589024476
81 82 83 84 85
0.0199488879 0.0838927059 -0.0521168025 0.0125216329 0.0699085049
86 87 88 89 90
-0.0708358630 0.1249606900 0.0487457426 0.0755660621 -0.0045940083
91 92 93 94 95
0.0340172653 0.0388029840 0.1236892936 0.0385961778 0.0881863813
96 97 98 99 100
0.0338294777 0.0617181868 -0.0747703890 -0.0743335424 -0.2578076632
101 102 103
-0.0617919371 -0.0899012372 0.1000954144
> postscript(file="/var/www/rcomp/tmp/61mxl1291887467.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 = 103
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.0400679410 NA
1 0.0184683021 -0.0400679410
2 0.0444392530 0.0184683021
3 -0.0497424806 0.0444392530
4 0.0387964310 -0.0497424806
5 -0.2337168788 0.0387964310
6 0.0426191914 -0.2337168788
7 0.0649238030 0.0426191914
8 -0.1064300476 0.0649238030
9 0.0355938567 -0.1064300476
10 0.1196550236 0.0355938567
11 -0.0682831511 0.1196550236
12 -0.0760979821 -0.0682831511
13 0.0147733718 -0.0760979821
14 0.0032111763 0.0147733718
15 -0.1428894996 0.0032111763
16 -0.0729903013 -0.1428894996
17 0.0082599417 -0.0729903013
18 0.0259425302 0.0082599417
19 -0.0145360060 0.0259425302
20 0.1518548894 -0.0145360060
21 0.0204663843 0.1518548894
22 -0.0459613393 0.0204663843
23 -0.0348719885 -0.0459613393
24 0.0983729320 -0.0348719885
25 0.0721420100 0.0983729320
26 0.1089833343 0.0721420100
27 -0.0125240011 0.1089833343
28 -0.0474558610 -0.0125240011
29 0.1149584182 -0.0474558610
30 0.0594956047 0.1149584182
31 0.0673260574 0.0594956047
32 -0.0499978928 0.0673260574
33 -0.0959527967 -0.0499978928
34 0.0068267636 -0.0959527967
35 0.0056276183 0.0068267636
36 0.0005322508 0.0056276183
37 0.0072493419 0.0005322508
38 -0.1319867014 0.0072493419
39 0.0792288094 -0.1319867014
40 -0.0465066061 0.0792288094
41 0.0052201807 -0.0465066061
42 0.0139478585 0.0052201807
43 -0.0588271592 0.0139478585
44 0.0293957856 -0.0588271592
45 -0.0013157252 0.0293957856
46 0.0867169267 -0.0013157252
47 -0.0698960595 0.0867169267
48 0.0729810706 -0.0698960595
49 0.0717805561 0.0729810706
50 -0.0073067109 0.0717805561
51 0.0871729876 -0.0073067109
52 -0.1643334282 0.0871729876
53 -0.1004490102 -0.1643334282
54 0.0890945086 -0.1004490102
55 -0.0181778108 0.0890945086
56 0.1149657132 -0.0181778108
57 -0.0559728751 0.1149657132
58 -0.0261681460 -0.0559728751
59 0.0766485209 -0.0261681460
60 0.0188522155 0.0766485209
61 0.0701224719 0.0188522155
62 -0.0485393891 0.0701224719
63 -0.0837245513 -0.0485393891
64 -0.0410909451 -0.0837245513
65 0.0111863484 -0.0410909451
66 0.0594691602 0.0111863484
67 -0.0956444666 0.0594691602
68 -0.0033576611 -0.0956444666
69 0.0449915381 -0.0033576611
70 0.0258874984 0.0449915381
71 -0.0343296364 0.0258874984
72 0.1091162912 -0.0343296364
73 -0.0323604695 0.1091162912
74 -0.1819650628 -0.0323604695
75 -0.0477441173 -0.1819650628
76 -0.0403319129 -0.0477441173
77 0.0205914182 -0.0403319129
78 -0.1635721455 0.0205914182
79 0.0589024476 -0.1635721455
80 0.0199488879 0.0589024476
81 0.0838927059 0.0199488879
82 -0.0521168025 0.0838927059
83 0.0125216329 -0.0521168025
84 0.0699085049 0.0125216329
85 -0.0708358630 0.0699085049
86 0.1249606900 -0.0708358630
87 0.0487457426 0.1249606900
88 0.0755660621 0.0487457426
89 -0.0045940083 0.0755660621
90 0.0340172653 -0.0045940083
91 0.0388029840 0.0340172653
92 0.1236892936 0.0388029840
93 0.0385961778 0.1236892936
94 0.0881863813 0.0385961778
95 0.0338294777 0.0881863813
96 0.0617181868 0.0338294777
97 -0.0747703890 0.0617181868
98 -0.0743335424 -0.0747703890
99 -0.2578076632 -0.0743335424
100 -0.0617919371 -0.2578076632
101 -0.0899012372 -0.0617919371
102 0.1000954144 -0.0899012372
103 NA 0.1000954144
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] 0.0184683021 -0.0400679410
[2,] 0.0444392530 0.0184683021
[3,] -0.0497424806 0.0444392530
[4,] 0.0387964310 -0.0497424806
[5,] -0.2337168788 0.0387964310
[6,] 0.0426191914 -0.2337168788
[7,] 0.0649238030 0.0426191914
[8,] -0.1064300476 0.0649238030
[9,] 0.0355938567 -0.1064300476
[10,] 0.1196550236 0.0355938567
[11,] -0.0682831511 0.1196550236
[12,] -0.0760979821 -0.0682831511
[13,] 0.0147733718 -0.0760979821
[14,] 0.0032111763 0.0147733718
[15,] -0.1428894996 0.0032111763
[16,] -0.0729903013 -0.1428894996
[17,] 0.0082599417 -0.0729903013
[18,] 0.0259425302 0.0082599417
[19,] -0.0145360060 0.0259425302
[20,] 0.1518548894 -0.0145360060
[21,] 0.0204663843 0.1518548894
[22,] -0.0459613393 0.0204663843
[23,] -0.0348719885 -0.0459613393
[24,] 0.0983729320 -0.0348719885
[25,] 0.0721420100 0.0983729320
[26,] 0.1089833343 0.0721420100
[27,] -0.0125240011 0.1089833343
[28,] -0.0474558610 -0.0125240011
[29,] 0.1149584182 -0.0474558610
[30,] 0.0594956047 0.1149584182
[31,] 0.0673260574 0.0594956047
[32,] -0.0499978928 0.0673260574
[33,] -0.0959527967 -0.0499978928
[34,] 0.0068267636 -0.0959527967
[35,] 0.0056276183 0.0068267636
[36,] 0.0005322508 0.0056276183
[37,] 0.0072493419 0.0005322508
[38,] -0.1319867014 0.0072493419
[39,] 0.0792288094 -0.1319867014
[40,] -0.0465066061 0.0792288094
[41,] 0.0052201807 -0.0465066061
[42,] 0.0139478585 0.0052201807
[43,] -0.0588271592 0.0139478585
[44,] 0.0293957856 -0.0588271592
[45,] -0.0013157252 0.0293957856
[46,] 0.0867169267 -0.0013157252
[47,] -0.0698960595 0.0867169267
[48,] 0.0729810706 -0.0698960595
[49,] 0.0717805561 0.0729810706
[50,] -0.0073067109 0.0717805561
[51,] 0.0871729876 -0.0073067109
[52,] -0.1643334282 0.0871729876
[53,] -0.1004490102 -0.1643334282
[54,] 0.0890945086 -0.1004490102
[55,] -0.0181778108 0.0890945086
[56,] 0.1149657132 -0.0181778108
[57,] -0.0559728751 0.1149657132
[58,] -0.0261681460 -0.0559728751
[59,] 0.0766485209 -0.0261681460
[60,] 0.0188522155 0.0766485209
[61,] 0.0701224719 0.0188522155
[62,] -0.0485393891 0.0701224719
[63,] -0.0837245513 -0.0485393891
[64,] -0.0410909451 -0.0837245513
[65,] 0.0111863484 -0.0410909451
[66,] 0.0594691602 0.0111863484
[67,] -0.0956444666 0.0594691602
[68,] -0.0033576611 -0.0956444666
[69,] 0.0449915381 -0.0033576611
[70,] 0.0258874984 0.0449915381
[71,] -0.0343296364 0.0258874984
[72,] 0.1091162912 -0.0343296364
[73,] -0.0323604695 0.1091162912
[74,] -0.1819650628 -0.0323604695
[75,] -0.0477441173 -0.1819650628
[76,] -0.0403319129 -0.0477441173
[77,] 0.0205914182 -0.0403319129
[78,] -0.1635721455 0.0205914182
[79,] 0.0589024476 -0.1635721455
[80,] 0.0199488879 0.0589024476
[81,] 0.0838927059 0.0199488879
[82,] -0.0521168025 0.0838927059
[83,] 0.0125216329 -0.0521168025
[84,] 0.0699085049 0.0125216329
[85,] -0.0708358630 0.0699085049
[86,] 0.1249606900 -0.0708358630
[87,] 0.0487457426 0.1249606900
[88,] 0.0755660621 0.0487457426
[89,] -0.0045940083 0.0755660621
[90,] 0.0340172653 -0.0045940083
[91,] 0.0388029840 0.0340172653
[92,] 0.1236892936 0.0388029840
[93,] 0.0385961778 0.1236892936
[94,] 0.0881863813 0.0385961778
[95,] 0.0338294777 0.0881863813
[96,] 0.0617181868 0.0338294777
[97,] -0.0747703890 0.0617181868
[98,] -0.0743335424 -0.0747703890
[99,] -0.2578076632 -0.0743335424
[100,] -0.0617919371 -0.2578076632
[101,] -0.0899012372 -0.0617919371
[102,] 0.1000954144 -0.0899012372
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 0.0184683021 -0.0400679410
2 0.0444392530 0.0184683021
3 -0.0497424806 0.0444392530
4 0.0387964310 -0.0497424806
5 -0.2337168788 0.0387964310
6 0.0426191914 -0.2337168788
7 0.0649238030 0.0426191914
8 -0.1064300476 0.0649238030
9 0.0355938567 -0.1064300476
10 0.1196550236 0.0355938567
11 -0.0682831511 0.1196550236
12 -0.0760979821 -0.0682831511
13 0.0147733718 -0.0760979821
14 0.0032111763 0.0147733718
15 -0.1428894996 0.0032111763
16 -0.0729903013 -0.1428894996
17 0.0082599417 -0.0729903013
18 0.0259425302 0.0082599417
19 -0.0145360060 0.0259425302
20 0.1518548894 -0.0145360060
21 0.0204663843 0.1518548894
22 -0.0459613393 0.0204663843
23 -0.0348719885 -0.0459613393
24 0.0983729320 -0.0348719885
25 0.0721420100 0.0983729320
26 0.1089833343 0.0721420100
27 -0.0125240011 0.1089833343
28 -0.0474558610 -0.0125240011
29 0.1149584182 -0.0474558610
30 0.0594956047 0.1149584182
31 0.0673260574 0.0594956047
32 -0.0499978928 0.0673260574
33 -0.0959527967 -0.0499978928
34 0.0068267636 -0.0959527967
35 0.0056276183 0.0068267636
36 0.0005322508 0.0056276183
37 0.0072493419 0.0005322508
38 -0.1319867014 0.0072493419
39 0.0792288094 -0.1319867014
40 -0.0465066061 0.0792288094
41 0.0052201807 -0.0465066061
42 0.0139478585 0.0052201807
43 -0.0588271592 0.0139478585
44 0.0293957856 -0.0588271592
45 -0.0013157252 0.0293957856
46 0.0867169267 -0.0013157252
47 -0.0698960595 0.0867169267
48 0.0729810706 -0.0698960595
49 0.0717805561 0.0729810706
50 -0.0073067109 0.0717805561
51 0.0871729876 -0.0073067109
52 -0.1643334282 0.0871729876
53 -0.1004490102 -0.1643334282
54 0.0890945086 -0.1004490102
55 -0.0181778108 0.0890945086
56 0.1149657132 -0.0181778108
57 -0.0559728751 0.1149657132
58 -0.0261681460 -0.0559728751
59 0.0766485209 -0.0261681460
60 0.0188522155 0.0766485209
61 0.0701224719 0.0188522155
62 -0.0485393891 0.0701224719
63 -0.0837245513 -0.0485393891
64 -0.0410909451 -0.0837245513
65 0.0111863484 -0.0410909451
66 0.0594691602 0.0111863484
67 -0.0956444666 0.0594691602
68 -0.0033576611 -0.0956444666
69 0.0449915381 -0.0033576611
70 0.0258874984 0.0449915381
71 -0.0343296364 0.0258874984
72 0.1091162912 -0.0343296364
73 -0.0323604695 0.1091162912
74 -0.1819650628 -0.0323604695
75 -0.0477441173 -0.1819650628
76 -0.0403319129 -0.0477441173
77 0.0205914182 -0.0403319129
78 -0.1635721455 0.0205914182
79 0.0589024476 -0.1635721455
80 0.0199488879 0.0589024476
81 0.0838927059 0.0199488879
82 -0.0521168025 0.0838927059
83 0.0125216329 -0.0521168025
84 0.0699085049 0.0125216329
85 -0.0708358630 0.0699085049
86 0.1249606900 -0.0708358630
87 0.0487457426 0.1249606900
88 0.0755660621 0.0487457426
89 -0.0045940083 0.0755660621
90 0.0340172653 -0.0045940083
91 0.0388029840 0.0340172653
92 0.1236892936 0.0388029840
93 0.0385961778 0.1236892936
94 0.0881863813 0.0385961778
95 0.0338294777 0.0881863813
96 0.0617181868 0.0338294777
97 -0.0747703890 0.0617181868
98 -0.0743335424 -0.0747703890
99 -0.2578076632 -0.0743335424
100 -0.0617919371 -0.2578076632
101 -0.0899012372 -0.0617919371
102 0.1000954144 -0.0899012372
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/7udf61291887467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/8m4w91291887467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/rcomp/tmp/9m4w91291887467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/rcomp/tmp/10m4w91291887467.ps",horizontal=F,onefile=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/11q4cw1291887467.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/12t5t31291887467.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/13068e1291887467.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/rcomp/tmp/14bfpz1291887467.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/15wyo51291887467.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/rcomp/tmp/16ig4t1291887467.tab")
+ }
>
> try(system("convert tmp/1qcyi1291887467.ps tmp/1qcyi1291887467.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qcyi1291887467.ps tmp/2qcyi1291887467.png",intern=TRUE))
character(0)
> try(system("convert tmp/3qcyi1291887467.ps tmp/3qcyi1291887467.png",intern=TRUE))
character(0)
> try(system("convert tmp/41mxl1291887467.ps tmp/41mxl1291887467.png",intern=TRUE))
character(0)
> try(system("convert tmp/51mxl1291887467.ps tmp/51mxl1291887467.png",intern=TRUE))
character(0)
> try(system("convert tmp/61mxl1291887467.ps tmp/61mxl1291887467.png",intern=TRUE))
character(0)
> try(system("convert tmp/7udf61291887467.ps tmp/7udf61291887467.png",intern=TRUE))
character(0)
> try(system("convert tmp/8m4w91291887467.ps tmp/8m4w91291887467.png",intern=TRUE))
character(0)
> try(system("convert tmp/9m4w91291887467.ps tmp/9m4w91291887467.png",intern=TRUE))
character(0)
> try(system("convert tmp/10m4w91291887467.ps tmp/10m4w91291887467.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
4.200 0.870 5.057