downjones en iraq

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 20 Nov 2008 12:16:56 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6.htm/, Retrieved Thu, 20 Nov 2008 19:17:52 +0000

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/2008/Nov/20/t12272086638zntx0kgl0c31a6.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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10433,56 0 10665,78 0 10666,71 0 10682,74 0 10777,22 0 10052,6 0 10213,97 0 10546,82 0 10767,2 0 10444,5 0 10314,68 0 9042,56 0 9220,75 0 9721,84 0 9978,53 0 9923,81 0 9892,56 0 10500,98 0 10179,35 0 10080,48 0 9492,44 0 8616,49 0 8685,4 0 8160,67 0 8048,1 0 8641,21 0 8526,63 0 8474,21 0 7916,13 0 7977,64 1 8334,59 1 8623,36 1 9098,03 1 9154,34 1 9284,73 1 9492,49 1 9682,35 1 9762,12 1 10124,63 1 10540,05 1 10601,61 1 10323,73 1 10418,4 1 10092,96 1 10364,91 1 10152,09 1 10032,8 1 10204,59 1 10001,6 1 10411,75 1 10673,38 1 10539,51 1 10723,78 1 10682,06 1 10283,19 1 10377,18 1 10486,64 1 10545,38 1 10554,27 1 10532,54 1 10324,31 1 10695,25 1 10827,81 1 10872,48 1 10971,19 1 11145,65 1 11234,68 1 11333,88 1 10997,97 1 11036,89 1 11257,35 1 11533,59 1 11963,12 1 12185,15 1 12377,62 1 12512,89 1 12631,48 1 12268,53 1 12754,8 1 13407,75 1 13480,21 1 13673,28 1 13239,71 1 13557,69 1 13901,28 1 13200,58 1 13406,97 1 12538,12 1 12419,57 1 12193,88 1 12656,63 1 12812,48 1 12056,67 1 11322,38 1 11530,75 1 11114,08 1 9181,73 1

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 3 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation downjones[t] = + 8560.16107888631 -802.32169180201iraq[t] + 15.8188349392086M1[t] + 567.68176814793M2[t] + 683.777945185616M3[t] + 575.240372223296M4[t] + 510.227799260977M5[t] + 465.730437773911M6[t] + 535.819114811593M7[t] + 639.502791849274M8[t] + 526.918968886957M9[t] + 255.850145924637M10[t] + 203.913822962318M11[t] + 46.2288229623185t + 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) 8560.16107888631 417.584118 20.4992 0 0 iraq -802.32169180201 378.089614 -2.122 0.036815 0.018408 M1 15.8188349392086 501.217668 0.0316 0.974898 0.487449 M2 567.68176814793 516.70661 1.0987 0.275096 0.137548 M3 683.777945185616 516.45443 1.324 0.189143 0.094571 M4 575.240372223296 516.275806 1.1142 0.268405 0.134202 M5 510.227799260977 516.170812 0.9885 0.325787 0.162894 M6 465.730437773911 516.661521 0.9014 0.369972 0.184986 M7 535.819114811593 516.256466 1.0379 0.302334 0.151167 M8 639.502791849274 515.924822 1.2395 0.218644 0.109322 M9 526.918968886957 515.666728 1.0218 0.309835 0.154917 M10 255.850145924637 515.482297 0.4963 0.620972 0.310486 M11 203.913822962318 515.371606 0.3957 0.693368 0.346684 t 46.2288229623185 6.167274 7.4958 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.735788599043859 R-squared 0.541384862482925 Adjusted R-squared 0.469553575883865 F-TEST (value) 7.53689496757544 F-TEST (DF numerator) 13 F-TEST (DF denominator) 83 p-value 1.41990563751193e-09 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1030.66940860170 Sum Squared Residuals 88169192.6756715 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10433.56 8622.2087367878 1811.35126321220 2 10665.78 9220.3004929589 1445.47950704110 3 10666.71 9382.62549295889 1284.08450704111 4 10682.74 9320.31674295888 1362.42325704112 5 10777.22 9301.53299295888 1475.68700704111 6 10052.6 9303.26445443413 749.335545565866 7 10213.97 9419.58195443413 794.388045565867 8 10546.82 9569.49445443413 977.325545565867 9 10767.2 9503.13945443413 1264.06054556587 10 10444.5 9278.29945443413 1166.20054556587 11 10314.68 9272.59195443413 1042.08804556587 12 9042.56 9114.90695443413 -72.3469544341336 13 9220.75 9176.95461233566 43.7953876643423 14 9721.84 9775.0463685067 -53.2063685066969 15 9978.53 9937.3713685067 41.1586314932972 16 9923.81 9875.0626185067 48.7473814932964 17 9892.56 9856.2788685067 36.2811314932963 18 10500.98 9858.01032998195 642.969670018045 19 10179.35 9974.32782998196 205.022170018045 20 10080.48 10124.2403299820 -43.7603299819557 21 9492.44 10057.8853299820 -565.445329981955 22 8616.49 9833.04532998195 -1216.55532998196 23 8685.4 9827.33782998195 -1141.93782998196 24 8160.67 9669.65282998196 -1508.98282998196 25 8048.1 9731.70048788348 -1683.60048788348 26 8641.21 10329.7922440545 -1688.58224405452 27 8526.63 10492.1172440545 -1965.48724405453 28 8474.21 10429.8084940545 -1955.59849405453 29 7916.13 10411.0247440545 -2494.89474405452 30 7977.64 9610.43451372776 -1632.79451372776 31 8334.59 9726.75201372776 -1392.16201372776 32 8623.36 9876.66451372776 -1253.30451372776 33 9098.03 9810.30951372776 -712.279513727763 34 9154.34 9585.46951372776 -431.129513727764 35 9284.73 9579.76201372776 -295.032013727764 36 9492.49 9422.07701372777 70.4129862722353 37 9682.35 9484.1246716293 198.225328370711 38 9762.12 10082.2164278003 -320.096427800332 39 10124.63 10244.5414278003 -119.911427800336 40 10540.05 10182.2326778003 357.817322199665 41 10601.61 10163.4489278003 438.161072199666 42 10323.73 10165.1803892756 158.549610724414 43 10418.4 10281.4978892756 136.902110724414 44 10092.96 10431.4103892756 -338.450389275587 45 10364.91 10365.0553892756 -0.145389275586069 46 10152.09 10140.2153892756 11.8746107244138 47 10032.8 10134.5078892756 -101.707889275587 48 10204.59 9976.82288927559 227.767110724414 49 10001.6 10038.8705471771 -37.270547177111 50 10411.75 10636.9623033482 -225.212303348155 51 10673.38 10799.2873033482 -125.907303348157 52 10539.51 10736.9785533482 -197.468553348156 53 10723.78 10718.1948033482 5.58519665184457 54 10682.06 10719.9262648234 -37.8662648234082 55 10283.19 10836.2437648234 -553.053764823407 56 10377.18 10986.1562648234 -608.976264823408 57 10486.64 10919.8012648234 -433.161264823408 58 10545.38 10694.9612648234 -149.581264823409 59 10554.27 10689.2537648234 -134.983764823407 60 10532.54 10531.5687648234 0.97123517659304 61 10324.31 10593.6164227249 -269.306422724933 62 10695.25 11191.7081788960 -496.458178895976 63 10827.81 11354.0331788960 -526.223178895978 64 10872.48 11291.7244288960 -419.244428895978 65 10971.19 11272.9406788960 -301.750678895977 66 11145.65 11274.6721403712 -129.022140371230 67 11234.68 11390.9896403712 -156.309640371229 68 11333.88 11540.9021403712 -207.022140371230 69 10997.97 11474.5471403712 -476.57714037123 70 11036.89 11249.7071403712 -212.817140371230 71 11257.35 11243.9996403712 13.3503596287711 72 11533.59 11086.3146403712 447.27535962877 73 11963.12 11148.3622982728 814.757701727246 74 12185.15 11746.4540544438 438.695945556202 75 12377.62 11908.7790544438 468.840945556201 76 12512.89 11846.4703044438 666.4196955562 77 12631.48 11827.6865544438 803.7934455562 78 12268.53 11829.4180159191 439.111984080949 79 12754.8 11945.7355159191 809.064484080948 80 13407.75 12095.6480159191 1312.10198408095 81 13480.21 12029.2930159191 1450.91698408095 82 13673.28 11804.4530159191 1868.82698408095 83 13239.71 11798.7455159191 1440.96448408095 84 13557.69 11641.0605159191 1916.62948408095 85 13901.28 11703.1081738206 2198.17182617942 86 13200.58 12301.1999299916 899.38007000838 87 13406.97 12463.5249299916 943.445070008378 88 12538.12 12401.2161799916 136.90382000838 89 12419.57 12382.4324299916 37.1375700083781 90 12193.88 12384.1638914669 -190.283891466874 91 12656.63 12500.4813914669 156.148608533126 92 12812.48 12650.3938914669 162.086108533127 93 12056.67 12584.0388914669 -527.368891466873 94 11322.38 12359.1988914669 -1036.81889146687 95 11530.75 12353.4913914669 -822.741391466873 96 11114.08 12195.8063914669 -1081.72639146687 97 9181.73 12257.8540493684 -3076.1240493684 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0085013469880439 0.0170026939760878 0.991498653011956 18 0.106955342344478 0.213910684688955 0.893044657655522 19 0.0759157196981441 0.151831439396288 0.924084280301856 20 0.0388971804332912 0.0777943608665824 0.961102819566709 21 0.0317428962924985 0.063485792584997 0.968257103707501 22 0.0509515551026803 0.101903110205361 0.94904844489732 23 0.0466538908289748 0.0933077816579495 0.953346109171025 24 0.0246492757819277 0.0492985515638554 0.975350724218072 25 0.0160776697301509 0.0321553394603018 0.98392233026985 26 0.00835121754497924 0.0167024350899585 0.99164878245502 27 0.00476621366677503 0.00953242733355007 0.995233786333225 28 0.00267341373720656 0.00534682747441313 0.997326586262793 29 0.00330246041735260 0.00660492083470521 0.996697539582647 30 0.00194903391557484 0.00389806783114968 0.998050966084425 31 0.00124864264892609 0.00249728529785219 0.998751357351074 32 0.000788513489609663 0.00157702697921933 0.99921148651039 33 0.000896073828991946 0.00179214765798389 0.999103926171008 34 0.00226984530918448 0.00453969061836896 0.997730154690815 35 0.00378699280642315 0.0075739856128463 0.996213007193577 36 0.0252066960107671 0.0504133920215341 0.974793303989233 37 0.066954960465339 0.133909920930678 0.933045039534661 38 0.0761905887962955 0.152381177592591 0.923809411203705 39 0.0929074546344665 0.185814909268933 0.907092545365533 40 0.132987411763079 0.265974823526158 0.867012588236921 41 0.179889971153842 0.359779942307683 0.820110028846158 42 0.227081185635283 0.454162371270565 0.772918814364717 43 0.259529589495929 0.519059178991859 0.740470410504071 44 0.239172214398694 0.478344428797387 0.760827785601306 45 0.228097609639243 0.456195219278486 0.771902390360757 46 0.221601265957518 0.443202531915037 0.778398734042482 47 0.201064946712541 0.402129893425082 0.798935053287459 48 0.221267344263473 0.442534688526946 0.778732655736527 49 0.204386406268404 0.408772812536808 0.795613593731596 50 0.186426594662523 0.372853189325046 0.813573405337477 51 0.170216273338131 0.340432546676261 0.82978372666187 52 0.143372578989789 0.286745157979577 0.856627421010211 53 0.125588475859554 0.251176951719107 0.874411524140446 54 0.115243272945697 0.230486545891394 0.884756727054303 55 0.0962693015933403 0.192538603186681 0.90373069840666 56 0.08367134658472 0.16734269316944 0.91632865341528 57 0.066720171920953 0.133440343841906 0.933279828079047 58 0.0554220038618922 0.110844007723784 0.944577996138108 59 0.0450489098014665 0.090097819602933 0.954951090198534 60 0.0398575611404165 0.079715122280833 0.960142438859583 61 0.0296758728770715 0.059351745754143 0.970324127122928 62 0.0268808982520334 0.0537617965040668 0.973119101747967 63 0.025792854574866 0.051585709149732 0.974207145425134 64 0.0218388616409387 0.0436777232818774 0.978161138359061 65 0.0187636434899780 0.0375272869799560 0.981236356510022 66 0.0149813792066507 0.0299627584133014 0.98501862079335 67 0.0142134882699058 0.0284269765398115 0.985786511730094 68 0.0172057943711028 0.0344115887422055 0.982794205628897 69 0.0224684346796041 0.0449368693592082 0.977531565320396 70 0.0302947361005227 0.0605894722010455 0.969705263899477 71 0.0417145285589686 0.0834290571179372 0.958285471441031 72 0.0649279826386343 0.129855965277269 0.935072017361366 73 0.0601906002237631 0.120381200447526 0.939809399776237 74 0.0853210153573041 0.170642030714608 0.914678984642696 75 0.14094351077846 0.28188702155692 0.85905648922154 76 0.143359981477905 0.28671996295581 0.856640018522095 77 0.139536515284983 0.279073030569967 0.860463484715017 78 0.154535246676829 0.309070493353657 0.845464753323171 79 0.215512875306959 0.431025750613917 0.784487124693041 80 0.277291677688821 0.554583355377642 0.722708322311179 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.140625 NOK 5% type I error level 19 0.296875 NOK 10% type I error level 30 0.46875 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/10pszf1227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/10pszf1227208611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/1buvc1227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/1buvc1227208611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/2hbzg1227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/2hbzg1227208611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/3k2r61227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/3k2r61227208611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/4t8nz1227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/4t8nz1227208611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/5emrj1227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/5emrj1227208611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/6p5ud1227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/6p5ud1227208611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/7cq681227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/7cq681227208611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/8cs371227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/8cs371227208611.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/9gp3r1227208611.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t12272086638zntx0kgl0c31a6/9gp3r1227208611.ps (open in new window)

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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