Time Series Analysis WS8 - Lineaire Trend

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 26 Nov 2010 08:01:28 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/26/t12907584850l1h0ac3hde65jv.htm/, Retrieved Fri, 26 Nov 2010 09:01:35 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Nov/26/t12907584850l1h0ac3hde65jv.htm/}, year = {2010}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2010}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

43880 25222 43110 21333 44496 19778 44164 25943 40399 21698 36763 20077 37903 25673 35532 19094 35533 19306 32110 15443 33374 15179 35462 18288 33508 18264 36080 16406 34560 15678 38737 19657 38144 18821 37594 19493 36424 21078 36843 19296 37246 19985 38661 16972 40454 16951 44928 23126 48441 24890 48140 21042 45998 20842 47369 23904 49554 22578 47510 25452 44873 21928 45344 25227 42413 26210 36912 17436 43452 21258 42142 25638 44382 23516 43636 23891 44167 24617 44423 26174 42868 23339 43908 23660 42013 26500 38846 22469 35087 23163 33026 16170 34646 18267 37135 20561 37985 20372 43121 19017 43722 18242 43630 20937 42234 22065 39351 16731 39327 21943 35704 19254 30466 16397 28155 13644 29257 14375 29998 14814 32529 16061 34787 14784 33855 12824 34556 18282 31348 14936 30805 15701 28353 16394 24514 13085 21106 11431 21346 9334 23335 10921 24379 11725 26290 13077 30084 11794 29429 11047 30632 16797 27349 11482 27264 12657 27474 15277 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 29 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation OPENVAC[t] = + 1845.12534861158 + 1.62106650935412OntvangenJobs[t] + 1398.53510581224M1[t] + 6074.8953918953M2[t] + 7089.78142291843M3[t] + 2738.34574444754M4[t] + 3642.41998058522M5[t] + 3207.92209473057M6[t] -1101.43725899311M7[t] -196.286404771903M8[t] -1715.09730769288M9[t] + 3278.45278852018M10[t] + 2723.69286893376M11[t] + 54.6959568227012t + e[t] Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STAT H0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 1845.12534861158 2490.08811 0.741 0.460211 0.230106 OntvangenJobs 1.62106650935412 0.095028 17.0588 0 0 M1 1398.53510581224 1370.829419 1.0202 0.30977 0.154885 M2 6074.8953918953 1374.737171 4.419 2.3e-05 1.1e-05 M3 7089.78142291843 1381.267556 5.1328 1e-06 1e-06 M4 2738.34574444754 1377.411092 1.988 0.049183 0.024592 M5 3642.41998058522 1370.319506 2.6581 0.008978 0.004489 M6 3207.92209473057 1369.493966 2.3424 0.020879 0.010439 M7 -1101.43725899311 1389.479576 -0.7927 0.429586 0.214793 M8 -196.286404771903 1371.315133 -0.1431 0.886432 0.443216 M9 -1715.09730769288 1369.961069 -1.2519 0.213135 0.106568 M10 3278.45278852018 1456.501579 2.2509 0.026292 0.013146 M11 2723.69286893376 1423.720218 1.9131 0.058224 0.029112 t 54.6959568227012 13.216396 4.1385 6.7e-05 3.3e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.927238341877822 R-squared 0.859770942648332 Adjusted R-squared 0.843918962252056 F-TEST (value) 54.2374467514695 F-TEST (DF numerator) 13 F-TEST (DF denominator) 115 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3134.31537081502 Sum Squared Residuals 1129752277.02864 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 43880 44184.8959101759 -304.895910175923 2 43110 42611.6244982036 498.375501796364 3 44496 41160.4480640038 3335.55193599617 4 44164 46857.5833725238 -2693.58337252377 5 40399 40934.9262332759 -535.926233275927 6 36763 37927.3754925810 -1164.37549258096 7 37903 42744.2002820256 -4841.2002820256 8 35532 33039.0505280288 2492.94947197121 9 35533 31918.6016819136 3614.39831808643 10 32110 30704.6678093144 1405.33219068561 11 33374 29776.6422880812 3597.35771191881 12 35462 32147.5411535521 3314.45884644792 13 33508 33561.8666199625 -53.8666199625254 14 36080 35280.9812884883 799.01871151166 15 34560 35170.4268575244 -610.426857524371 16 38737 37323.9107765962 1413.08922340380 17 38144 36927.4693677366 1216.53063226345 18 37594 37637.0241329906 -43.0241329905656 19 36424 35951.7511534159 472.248846584139 20 36843 34022.8574447907 2820.14255520927 21 37246 33675.6573236374 3570.34267636256 22 38661 33839.6299839892 4821.37001601075 23 40454 33305.5236245291 7148.4763754709 24 44928 40646.6124076797 4281.38759232029 25 48441 44959.4047928153 3481.59520718469 26 48140 43452.5971077264 4687.40289227358 27 45998 44197.9657937014 1800.03420629856 28 47369 44864.9317236955 2504.06827630445 29 49554 43674.1677252524 5879.83227474762 30 47510 47953.3109441042 -443.310944104159 31 44873 37986.0091682393 6886.99083176073 32 45344 44293.7543936424 1050.24560635759 33 42413 44423.1478262392 -2010.14782623923 34 36912 35248.156326202 1663.84367379803 35 43452 40943.8085621897 2508.19143781031 36 42142 45375.0829610497 -3233.08296104966 37 44382 43388.4108908352 993.589109164828 38 43636 48727.3670747487 -5091.36707474871 39 44167 50973.8433483856 -6806.84334838564 40 44423 49201.1041818018 -4778.1041818018 41 42868 45564.1508207433 -2696.15082074328 42 43908 45704.711241214 -1796.71124121400 43 42013 46053.8767308787 -4040.87673087871 44 38846 40479.2044427162 -1633.20444271617 45 35087 40140.1096541096 -5053.10965410965 46 33026 33852.2376072321 -826.237607232074 47 34646 36751.5501145839 -2105.55011458394 48 37135 37801.2797749312 -666.279774931224 49 37985 38948.1292672982 -963.129267298244 50 43121 41482.6403900292 1638.35960997083 51 43722 41295.8958331256 2426.10416687443 52 43630 41367.9303541867 2262.06964581329 53 42234 44155.2635696986 -1921.26356969855 54 39351 35128.6928797717 4222.30712022826 55 39327 39323.0281296244 3.97187037558654 56 35704 35923.8270970151 -219.827097015101 57 30466 29828.3251336921 637.674866307889 58 28155 30413.775086476 -2258.77508647599 59 29257 31098.7107420501 -1841.71074205013 60 29998 29141.3620275455 856.637972454467 61 32529 32616.0630273451 -87.0630273450646 62 34787 35277.0173378056 -490.017337805609 63 33855 33169.3089673174 685.69103268262 64 34556 37720.3502537239 -3164.35025372395 65 31348 33255.0319063855 -1907.03190638547 66 30805 34115.3458570094 -3310.34585700942 67 28353 30984.0815510908 -2631.08155109084 68 24514 26579.819282682 -2065.81928268197 69 21106 22434.460330112 -1328.46033011198 70 21346 24083.3299130322 -2737.32991303217 71 23335 26155.8985006134 -2820.89850061343 72 24379 24790.2390620231 -411.239062023083 73 26290 28435.1520453048 -2145.15204530480 74 30084 31086.3799567092 -1002.37995670922 75 29429 30945.0252620675 -1516.02526206753 76 30632 35969.4179692055 -5337.4179692055 77 27349 28312.2196649488 -963.219664948766 78 27264 29837.1708844079 -2573.1708844079 79 27474 29829.7017420147 -2355.70174201470 80 24482 26101.4242080065 -1619.42420800650 81 21453 24006.7143897695 -2553.71438976947 82 18788 23217.4999426211 -4429.49994262106 83 19282 23581.4644293431 -4299.46442934309 84 19713 22548.1236251703 -2835.12362517034 85 21917 26483.2075136264 -4566.20751362644 86 23812 28111.5424576284 -4299.54245762841 87 23785 28649.4146304061 -4864.4146304061 88 24696 27262.4892930485 -2566.48929304854 89 24562 26671.5199030664 -2109.5199030664 90 23580 26087.4635938558 -2507.46359385583 91 24939 23842.9226685540 1096.07733144605 92 23899 26041.2642927444 -2142.2642927444 93 21454 22711.3017943795 -1257.30179437953 94 19761 23363.2154740469 -3602.21547404693 95 19815 23710.9692956754 -3895.96929567542 96 20780 25837.0871182338 -5057.08711823384 97 23462 25487.692222467 -2025.69222246701 98 25005 26568.1066863073 -1563.10668630729 99 24725 26673.1541010874 -1948.15410108743 100 26198 25487.2410108898 710.75898911022 101 27543 27890.3814636847 -347.381463684691 102 26471 28236.8173308434 -1765.81733084338 103 26558 25081.2370272845 1476.76297271551 104 25317 25849.7979902246 -532.797990224613 105 22896 22745.1637366600 150.836263340034 106 22248 20644.5064834441 1603.49351655592 107 23406 23966.9173497374 -560.917349737367 108 25073 24885.3406228270 187.659377173032 109 27691 24865.0222284590 2825.97777154098 110 30599 29018.9787940347 1580.02120596529 111 31948 29780.5581451033 2167.44185489673 112 32946 27717.6480733450 5228.35192665496 113 34012 31749.9603680408 2262.03963195916 114 32936 27503.9148141993 5432.08518580068 115 32974 29775.665183958 3198.33481604199 116 30951 28179.0901097505 2771.90989024952 117 29812 26520.4471825297 3291.5528174703 118 29010 24649.9813736421 4360.01862635791 119 31068 28797.5150931966 2270.48490680337 120 32447 28884.3312469876 3562.66875301243 121 34844 31999.1554817105 2844.84451828951 122 35676 32432.7644083185 3243.23559168152 123 35387 30055.9589972775 5331.04100272254 124 36488 30066.3929909832 6421.60700901685 125 35652 34529.9089771671 1122.09102283285 126 33488 29538.1728290227 3949.82717097727 127 32914 32179.5263629142 734.473637085839 128 29781 30702.9102103988 -921.91021039883 129 27951 27013.0709469573 937.929053042652 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.323829880182219 0.647659760364437 0.676170119817781 18 0.200390822803974 0.400781645607948 0.799609177196026 19 0.227462877473490 0.454925754946981 0.77253712252651 20 0.138276307916123 0.276552615832245 0.861723692083877 21 0.0823769048007595 0.164753809601519 0.91762309519924 22 0.0722249654535778 0.144449930907156 0.927775034546422 23 0.0673171707690334 0.134634341538067 0.932682829230967 24 0.0448992990795205 0.089798598159041 0.95510070092048 25 0.0296205976687470 0.0592411953374941 0.970379402331253 26 0.0224556351051517 0.0449112702103035 0.977544364894848 27 0.0206167572088977 0.0412335144177954 0.979383242791102 28 0.0129656498895207 0.0259312997790415 0.987034350110479 29 0.0207021457907781 0.0414042915815561 0.979297854209222 30 0.0207967663419983 0.0415935326839965 0.979203233658002 31 0.161160874129719 0.322321748259438 0.838839125870281 32 0.209468368672777 0.418936737345553 0.790531631327223 33 0.424034024175513 0.848068048351027 0.575965975824487 34 0.46791554718954 0.93583109437908 0.53208445281046 35 0.5391369912409 0.9217260175182 0.4608630087591 36 0.725381413433496 0.549237173133008 0.274618586566504 37 0.709564090653126 0.580871818693747 0.290435909346874 38 0.857955790525025 0.284088418949950 0.142044209474975 39 0.950894153797592 0.098211692404815 0.0491058462024075 40 0.96787573814042 0.0642485237191618 0.0321242618595809 41 0.969365351838383 0.0612692963232342 0.0306346481616171 42 0.959571183646588 0.0808576327068247 0.0404288163534123 43 0.965434102639243 0.0691317947215141 0.0345658973607571 44 0.960283973674914 0.0794320526501712 0.0397160263250856 45 0.98313940071741 0.033721198565182 0.016860599282591 46 0.980212575136515 0.0395748497269690 0.0197874248634845 47 0.983877082523623 0.0322458349527532 0.0161229174763766 48 0.978124533520945 0.0437509329581105 0.0218754664790552 49 0.970345331926335 0.0593093361473296 0.0296546680736648 50 0.966295281608442 0.067409436783116 0.033704718391558 51 0.967672022605818 0.0646559547883646 0.0323279773941823 52 0.96680832103351 0.0663833579329795 0.0331916789664898 53 0.95825425793387 0.0834914841322587 0.0417457420661293 54 0.979698018904433 0.0406039621911335 0.0203019810955667 55 0.972367202872072 0.0552655942558556 0.0276327971279278 56 0.968609752673091 0.0627804946538172 0.0313902473269086 57 0.968970612614061 0.0620587747718777 0.0310293873859389 58 0.969493941492788 0.0610121170144243 0.0305060585072121 59 0.975854714578015 0.0482905708439702 0.0241452854219851 60 0.982166323307945 0.0356673533841105 0.0178336766920552 61 0.98361207601196 0.0327758479760801 0.0163879239880400 62 0.984024082064544 0.0319518358709117 0.0159759179354558 63 0.988930013653536 0.0221399726929291 0.0110699863464646 64 0.986147364478312 0.0277052710433762 0.0138526355216881 65 0.985374161375177 0.0292516772496452 0.0146258386248226 66 0.982525679548063 0.0349486409038731 0.0174743204519365 67 0.978100593954272 0.0437988120914559 0.0218994060457280 68 0.97915624159641 0.0416875168071776 0.0208437584035888 69 0.979131698284273 0.0417366034314548 0.0208683017157274 70 0.97685236191595 0.0462952761680986 0.0231476380840493 71 0.980024001049106 0.0399519979017871 0.0199759989508935 72 0.987708778201167 0.0245824435976665 0.0122912217988332 73 0.987166859929447 0.0256662801411067 0.0128331400705534 74 0.991538393220454 0.0169232135590924 0.00846160677954619 75 0.99373485226139 0.0125302954772184 0.00626514773860918 76 0.991682893862085 0.0166342122758311 0.00831710613791554 77 0.993529702847845 0.0129405943043104 0.0064702971521552 78 0.992482439051902 0.0150351218961952 0.0075175609480976 79 0.992337938052286 0.0153241238954286 0.00766206194771431 80 0.996360329699454 0.00727934060109166 0.00363967030054583 81 0.997400388420997 0.00519922315800619 0.00259961157900309 82 0.996507120357193 0.00698575928561473 0.00349287964280736 83 0.995919622164581 0.00816075567083715 0.00408037783541858 84 0.994615167478363 0.0107696650432747 0.00538483252163737 85 0.992138579917874 0.0157228401642511 0.00786142008212553 86 0.988422186118271 0.0231556277634584 0.0115778138817292 87 0.983657138853106 0.0326857222937872 0.0163428611468936 88 0.976705117599643 0.0465897648007138 0.0232948824003569 89 0.96575185805485 0.0684962838903019 0.0342481419451509 90 0.950839363946667 0.0983212721066667 0.0491606360533333 91 0.956341611292994 0.0873167774140119 0.0436583887070060 92 0.953398450420204 0.0932030991595926 0.0466015495797963 93 0.95145521129229 0.097089577415419 0.0485447887077095 94 0.932781427528006 0.134437144943989 0.0672185724719944 95 0.911769524303432 0.176460951393136 0.0882304756965678 96 0.915361502796547 0.169276994406906 0.0846384972034532 97 0.898937446125708 0.202125107748585 0.101062553874292 98 0.874989690899789 0.250020618200423 0.125010309100211 99 0.889791638601435 0.22041672279713 0.110208361398565 100 0.9107920411438 0.1784159177124 0.0892079588562 101 0.878585964382755 0.242828071234491 0.121414035617245 102 0.96290302740494 0.074193945190119 0.0370969725950595 103 0.947780819273398 0.104438361453203 0.0522191807266017 104 0.92134078648998 0.157318427020041 0.0786592135100203 105 0.896021747853984 0.207956504292032 0.103978252146016 106 0.880158073250547 0.239683853498906 0.119841926749453 107 0.856792415952248 0.286415168095504 0.143207584047752 108 0.884624484531633 0.230751030936734 0.115375515468367 109 0.834218886567098 0.331562226865803 0.165781113432902 110 0.861763294506008 0.276473410987984 0.138236705493992 111 0.921244593521915 0.157510812956171 0.0787554064780854 112 0.968613618846565 0.0627727623068705 0.0313863811534352 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0416666666666667 NOK 5% type I error level 40 0.416666666666667 NOK 10% type I error level 64 0.666666666666667 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) 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 k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) 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)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z 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() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$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$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$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$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.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='mytable5.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='mytable6.tab') }

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