*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, 24 Dec 2010 13:05:44 +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/Dec/24/t1293195814qon3xblbaurkxq2.htm/, Retrieved Fri, 24 Dec 2010 14:03:45 +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/Dec/24/t1293195814qon3xblbaurkxq2.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:

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6,4 7,7 9,2 8,6 7,4 8,6 6,2 6 6,6 5,1 4,7 5 3,6 1,9 -0,1 -5,7 -5,6 -6,4 -7,7 -8 -11,9 -15,4 -15,5 -13,4 -10,9 -10,8 -7,3 -6,5 -5,1 -5,3 -6,8 -8,4 -8,4 -9,7 -8,8 -9,6 -11,5 -11 -14,9 -16,2 -14,4 -17,3 -15,7 -12,6 -9,4 -8,1 -5,4 -4,6 -4,9 -4 -3,1 -1,3 0 -0,4 3 0,4 1,2 0,6 -1,3 -3,2 -1,8 -3,6 -4,2 -6,9 -8 -7,5 -8,2 -7,6 -3,7 -1,7 -0,7 0,2 0,6 2,2 3,3 5,3 5,5 6,3 7,7 6,5 5,5 6,9 5,7 6,9 6,1 4,8 3,7 5,8 6,8 8,5 7,2 5 4,7 2,3 2,4 0,1 1,9 1,7 2 -1,9 0,5 -1,3 -3,3 -2,8 -8 -13,9 -21,9 -28,8 -27,6 -31,4 -31,8 -29,4 -27,6 -23,6 -22,8 -18,2 -17,8 -14,2 -8,8 -7,9 -7 -7 -3,6 -2,4 -4,9 -7,7 -6,5 -5,1 -3,4 -2,8 0,8

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 10 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Conjunctuur[t] = -1.90042553191490 + 1.15503223726628M1[t] + 0.810025789813028M2[t] + 1.11047388781432M3[t] + 0.820012894906516M4[t] + 1.34773372018053M5[t] + 1.33909090909092M6[t] + 1.32135718891038M7[t] + 1.56725983236622M8[t] + 1.64043520309478M9[t] + 1.12270148291425M10[t] + 1.36860412637009M11[t] -0.054993552546744t + 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) -1.90042553191490 3.341484 -0.5687 0.570615 0.285308 M1 1.15503223726628 4.157577 0.2778 0.781641 0.390821 M2 0.810025789813028 4.157051 0.1949 0.845841 0.42292 M3 1.11047388781432 4.156641 0.2672 0.789815 0.394908 M4 0.820012894906516 4.156349 0.1973 0.843939 0.421969 M5 1.34773372018053 4.156174 0.3243 0.746306 0.373153 M6 1.33909090909092 4.156115 0.3222 0.747873 0.373937 M7 1.32135718891038 4.156174 0.3179 0.751103 0.375551 M8 1.56725983236622 4.156349 0.3771 0.706794 0.353397 M9 1.64043520309478 4.156641 0.3947 0.693811 0.346905 M10 1.12270148291425 4.157051 0.2701 0.787577 0.393788 M11 1.36860412637009 4.157577 0.3292 0.742601 0.3713 t -0.054993552546744 0.022048 -2.4942 0.014008 0.007004 Multiple Linear Regression - Regression Statistics Multiple R 0.227359729148448 R-squared 0.0516924464384556 Adjusted R-squared -0.0447456098559387 F-TEST (value) 0.536017091433854 F-TEST (DF numerator) 12 F-TEST (DF denominator) 118 p-value 0.88736113578945 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9.51205301061793 Sum Squared Residuals 10676.5399922631 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6.4 -0.80038684719537 7.20038684719537 2 7.7 -1.20038684719534 8.90038684719534 3 9.2 -0.95493230174082 10.1549323017408 4 8.6 -1.30038684719534 9.90038684719534 5 7.4 -0.827659574468097 8.2276595744681 6 8.6 -0.89129593810445 9.49129593810445 7 6.2 -0.964023210831724 7.16402321083172 8 6 -0.77311411992264 6.77311411992264 9 6.6 -0.754932301740815 7.35493230174081 10 5.1 -1.32765957446809 6.42765957446809 11 4.7 -1.136750483559 5.836750483559 12 5 -2.56034816247583 7.56034816247583 13 3.6 -1.46030947775629 5.06030947775629 14 1.9 -1.86030947775629 3.76030947775629 15 -0.1 -1.61485493230174 1.51485493230174 16 -5.7 -1.96030947775628 -3.73969052224372 17 -5.6 -1.48758220502901 -4.11241779497099 18 -6.4 -1.55121856866538 -4.84878143133462 19 -7.7 -1.62394584139265 -6.07605415860735 20 -8 -1.43303675048356 -6.56696324951644 21 -11.9 -1.41485493230174 -10.4851450676983 22 -15.4 -1.98758220502901 -13.4124177949710 23 -15.5 -1.79667311411992 -13.7033268858801 24 -13.4 -3.22027079303675 -10.1797292069632 25 -10.9 -2.12023210831722 -8.77976789168278 26 -10.8 -2.52023210831721 -8.27976789168279 27 -7.3 -2.27477756286267 -5.02522243713733 28 -6.5 -2.62023210831721 -3.87976789168279 29 -5.1 -2.14750483558994 -2.95249516441006 30 -5.3 -2.21114119922630 -3.08885880077370 31 -6.8 -2.28386847195358 -4.51613152804642 32 -8.4 -2.09295938104449 -6.30704061895551 33 -8.4 -2.07477756286267 -6.32522243713734 34 -9.7 -2.64750483558994 -7.05249516441006 35 -8.8 -2.45659574468085 -6.34340425531915 36 -9.6 -3.88019342359768 -5.71980657640232 37 -11.5 -2.78015473887815 -8.71984526112185 38 -11 -3.18015473887814 -7.81984526112186 39 -14.9 -2.9347001934236 -11.9652998065764 40 -16.2 -3.28015473887814 -12.9198452611219 41 -14.4 -2.80742746615087 -11.5925725338491 42 -17.3 -2.87106382978723 -14.4289361702128 43 -15.7 -2.9437911025145 -12.7562088974855 44 -12.6 -2.75288201160542 -9.84711798839458 45 -9.4 -2.73470019342359 -6.6652998065764 46 -8.1 -3.30742746615087 -4.79257253384913 47 -5.4 -3.11651837524178 -2.28348162475822 48 -4.6 -4.54011605415861 -0.0598839458413878 49 -4.9 -3.44007736943908 -1.45992263056092 50 -4 -3.84007736943907 -0.159922630560931 51 -3.1 -3.59462282398453 0.494622823984528 52 -1.3 -3.94007736943907 2.64007736943907 53 0 -3.46735009671180 3.46735009671180 54 -0.4 -3.53098646034816 3.13098646034816 55 3 -3.60371373307544 6.60371373307544 56 0.4 -3.41280464216634 3.81280464216634 57 1.2 -3.39462282398452 4.59462282398452 58 0.6 -3.9673500967118 4.5673500967118 59 -1.3 -3.77644100580271 2.47644100580271 60 -3.2 -5.20003868471954 2.00003868471954 61 -1.8 -4.10000000000001 2.30000000000001 62 -3.6 -4.5 0.899999999999997 63 -4.2 -4.25454545454546 0.0545454545454559 64 -6.9 -4.59999999999999 -2.30000000000001 65 -8 -4.12727272727272 -3.87272727272728 66 -7.5 -4.19090909090909 -3.30909090909091 67 -8.2 -4.26363636363636 -3.93636363636363 68 -7.6 -4.07272727272727 -3.52727272727273 69 -3.7 -4.05454545454545 0.354545454545453 70 -1.7 -4.62727272727273 2.92727272727273 71 -0.7 -4.43636363636364 3.73636363636364 72 0.2 -5.85996131528047 6.05996131528047 73 0.6 -4.75992263056094 5.35992263056094 74 2.2 -5.15992263056093 7.35992263056093 75 3.3 -4.91446808510638 8.21446808510638 76 5.3 -5.25992263056092 10.5599226305609 77 5.5 -4.78719535783365 10.2871953578336 78 6.3 -4.85083172147002 11.1508317214700 79 7.7 -4.92355899419729 12.6235589941973 80 6.5 -4.7326499032882 11.2326499032882 81 5.5 -4.71446808510638 10.2144680851064 82 6.9 -5.28719535783366 12.1871953578337 83 5.7 -5.09628626692457 10.7962862669246 84 6.9 -6.5198839458414 13.4198839458414 85 6.1 -5.41984526112186 11.5198452611219 86 4.8 -5.81984526112186 10.6198452611219 87 3.7 -5.57439071566731 9.27439071566731 88 5.8 -5.91984526112185 11.7198452611219 89 6.8 -5.44711798839458 12.2471179883946 90 8.5 -5.51075435203094 14.0107543520309 91 7.2 -5.58348162475821 12.7834816247582 92 5 -5.39257253384913 10.3925725338491 93 4.7 -5.37439071566731 10.0743907156673 94 2.3 -5.94711798839459 8.24711798839459 95 2.4 -5.75620889748549 8.1562088974855 96 0.1 -7.17980657640233 7.27980657640233 97 1.9 -6.07976789168279 7.97976789168279 98 1.7 -6.47976789168278 8.17976789168278 99 2 -6.23431334622824 8.23431334622824 100 -1.9 -6.57976789168278 4.67976789168278 101 0.5 -6.10704061895551 6.60704061895551 102 -1.3 -6.17067698259187 4.87067698259187 103 -3.3 -6.24340425531915 2.94340425531915 104 -2.8 -6.05249516441006 3.25249516441006 105 -8 -6.03431334622824 -1.96568665377176 106 -13.9 -6.60704061895551 -7.29295938104449 107 -21.9 -6.41613152804642 -15.4838684719536 108 -28.8 -7.83972920696325 -20.9602707930367 109 -27.6 -6.73969052224371 -20.8603094777563 110 -31.4 -7.1396905222437 -24.2603094777563 111 -31.8 -6.89423597678917 -24.9057640232108 112 -29.4 -7.2396905222437 -22.1603094777563 113 -27.6 -6.76696324951644 -20.8330367504836 114 -23.6 -6.83059961315279 -16.7694003868472 115 -22.8 -6.90332688588007 -15.8966731141199 116 -18.2 -6.71241779497098 -11.4875822050290 117 -17.8 -6.69423597678916 -11.1057640232108 118 -14.2 -7.26696324951644 -6.93303675048356 119 -8.8 -7.07605415860735 -1.72394584139265 120 -7.9 -8.49965183752418 0.599651837524182 121 -7 -7.39961315280465 0.399613152804649 122 -7 -7.79961315280464 0.79961315280464 123 -3.6 -7.5541586073501 3.9541586073501 124 -2.4 -7.89961315280464 5.49961315280464 125 -4.9 -7.42688588007737 2.52688588007736 126 -7.7 -7.49052224371373 -0.209477756286270 127 -6.5 -7.563249516441 1.06324951644101 128 -5.1 -7.37234042553192 2.27234042553192 129 -3.4 -7.3541586073501 3.9541586073501 130 -2.8 -7.92688588007737 5.12688588007737 131 0.8 -7.73597678916828 8.53597678916828 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0596566112170021 0.119313222434004 0.940343388782998 17 0.0268585091998257 0.0537170183996513 0.973141490800174 18 0.0149357115747915 0.0298714231495830 0.985064288425209 19 0.00609473857402556 0.0121894771480511 0.993905261425974 20 0.00233663743988151 0.00467327487976301 0.997663362560119 21 0.00204482923471516 0.00408965846943032 0.997955170765285 22 0.00225682274697673 0.00451364549395347 0.997743177253023 23 0.00184022403667983 0.00368044807335967 0.99815977596332 24 0.000987477616569485 0.00197495523313897 0.99901252238343 25 0.000535971171600521 0.00107194234320104 0.9994640288284 26 0.000267673103813942 0.000535346207627885 0.999732326896186 27 0.000231546393589026 0.000463092787178052 0.999768453606411 28 0.00038690915674347 0.00077381831348694 0.999613090843257 29 0.000649088481975827 0.00129817696395165 0.999350911518024 30 0.000698585127881372 0.00139717025576274 0.999301414872119 31 0.000666216753804296 0.00133243350760859 0.999333783246196 32 0.000474152706143625 0.00094830541228725 0.999525847293856 33 0.000392737686638034 0.000785475373276069 0.999607262313362 34 0.000360325311948903 0.000720650623897805 0.999639674688051 35 0.000354219788060919 0.000708439576121838 0.99964578021194 36 0.00024718478128203 0.00049436956256406 0.999752815218718 37 0.000146970838118186 0.000293941676236372 0.999853029161882 38 8.70151699533093e-05 0.000174030339906619 0.999912984830047 39 4.99813704800139e-05 9.99627409600278e-05 0.99995001862952 40 3.03320884092612e-05 6.06641768185225e-05 0.99996966791159 41 1.81017860096253e-05 3.62035720192507e-05 0.99998189821399 42 1.31947446267686e-05 2.63894892535373e-05 0.999986805255373 43 9.4291395230195e-06 1.8858279046039e-05 0.999990570860477 44 8.36173193937206e-06 1.67234638787441e-05 0.99999163826806 45 1.31329145278071e-05 2.62658290556142e-05 0.999986867085472 46 3.5284912989641e-05 7.0569825979282e-05 0.99996471508701 47 0.000114450229961378 0.000228900459922755 0.999885549770039 48 0.000252439230979055 0.00050487846195811 0.99974756076902 49 0.000437019000402991 0.000874038000805982 0.999562980999597 50 0.000671761511453672 0.00134352302290734 0.999328238488546 51 0.00100264040039283 0.00200528080078566 0.998997359599607 52 0.00196896893071839 0.00393793786143678 0.998031031069282 53 0.00333899251564812 0.00667798503129624 0.996661007484352 54 0.00491184176066448 0.00982368352132897 0.995088158239335 55 0.00956840800605792 0.0191368160121158 0.990431591993942 56 0.0119867275941646 0.0239734551883292 0.988013272405835 57 0.0145773875354328 0.0291547750708656 0.985422612464567 58 0.0172999021385388 0.0345998042770776 0.982700097861461 59 0.0169859657148454 0.0339719314296909 0.983014034285155 60 0.0142519283696723 0.0285038567393446 0.985748071630328 61 0.0118227960030785 0.0236455920061571 0.988177203996921 62 0.00913588608623253 0.0182717721724651 0.990864113913767 63 0.0070369193854355 0.014073838770871 0.992963080614565 64 0.00573367023565442 0.0114673404713088 0.994266329764346 65 0.00497661886837559 0.00995323773675118 0.995023381131624 66 0.00447485310592425 0.0089497062118485 0.995525146894076 67 0.00431744346830816 0.00863488693661631 0.995682556531692 68 0.00445065949577475 0.0089013189915495 0.995549340504225 69 0.00416897451055084 0.00833794902110169 0.99583102548945 70 0.00409587782048031 0.00819175564096063 0.99590412217952 71 0.00405650297132396 0.00811300594264793 0.995943497028676 72 0.00352240715035424 0.00704481430070849 0.996477592849646 73 0.00288628665423309 0.00577257330846619 0.997113713345767 74 0.00242376277885144 0.00484752555770289 0.997576237221149 75 0.00210007895820995 0.00420015791641991 0.99789992104179 76 0.00214543916310164 0.00429087832620328 0.997854560836898 77 0.00205536861488916 0.00411073722977833 0.99794463138511 78 0.00203544752050611 0.00407089504101222 0.997964552479494 79 0.00213375867113428 0.00426751734226856 0.997866241328866 80 0.0019835093210841 0.0039670186421682 0.998016490678916 81 0.00161878582261112 0.00323757164522225 0.998381214177389 82 0.00142712929636985 0.0028542585927397 0.99857287070363 83 0.00112116497654941 0.00224232995309881 0.99887883502345 84 0.00105140123522058 0.00210280247044116 0.99894859876478 85 0.000826030723989983 0.00165206144797997 0.99917396927601 86 0.000617750224795856 0.00123550044959171 0.999382249775204 87 0.000420034645156019 0.000840069290312039 0.999579965354844 88 0.000332081696987066 0.000664163393974131 0.999667918303013 89 0.000276144636726656 0.000552289273453312 0.999723855363273 90 0.000280316260395727 0.000560632520791454 0.999719683739604 91 0.000261209307265077 0.000522418614530155 0.999738790692735 92 0.000192257121696183 0.000384514243392366 0.999807742878304 93 0.000147611084850408 0.000295222169700816 0.99985238891515 94 0.000104260626959023 0.000208521253918047 0.99989573937304 95 7.64331816434637e-05 0.000152866363286927 0.999923566818357 96 8.87338526142692e-05 0.000177467705228538 0.999911266147386 97 0.000128608144001671 0.000257216288003342 0.999871391855998 98 0.000282201438322041 0.000564402876644083 0.999717798561678 99 0.000729490920598833 0.00145898184119767 0.999270509079401 100 0.00131931045792236 0.00263862091584473 0.998680689542078 101 0.00463664605147914 0.00927329210295827 0.99536335394852 102 0.0183469015039509 0.0366938030079018 0.98165309849605 103 0.0750067652769789 0.150013530553958 0.92499323472302 104 0.309406311199694 0.618812622399388 0.690593688800306 105 0.73088037378886 0.538239252422281 0.269119626211140 106 0.958299673303777 0.0834006533924467 0.0417003266962233 107 0.97828132928696 0.0434373414260813 0.0217186707130407 108 0.975016895703392 0.0499662085932151 0.0249831042966076 109 0.96773533640151 0.0645293271969814 0.0322646635984907 110 0.96832410047615 0.0633517990476998 0.0316758995238499 111 0.985352859541377 0.0292942809172456 0.0146471404586228 112 0.996429668966804 0.00714066206639157 0.00357033103319579 113 0.999302987018834 0.00139402596233266 0.000697012981166328 114 0.997694845804596 0.0046103083908081 0.00230515419540405 115 0.995613916609643 0.00877216678071458 0.00438608339035729 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 76 0.76 NOK 5% type I error level 92 0.92 NOK 10% type I error level 96 0.96 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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Software written by Ed van Stee & Patrick Wessa

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