Mini-tutorial Ws 7

*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: Tue, 23 Nov 2010 19:58:46 +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/23/t12905422476wu67pvkodn2b31.htm/, Retrieved Tue, 23 Nov 2010 20:57:37 +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/23/t12905422476wu67pvkodn2b31.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 «

2649.2 31077 0 2579.4 31293 0 2504.6 30236 0 2462.3 30160 0 2467.4 32436 0 2446.7 30695 0 2656.3 27525 0 2626.2 26434 0 2482.6 25739 0 2539.9 25204 0 2502.7 24977 0 2466.9 24320 0 2513.2 22680 1 2443.3 22052 1 2293.4 21467 1 2070.8 21383 1 2029.6 21777 1 2052 21928 1 1864.4 21814 1 1670.1 22937 1 1811 23595 1 1905.4 20830 1 1862.8 19650 1 2014.5 19195 1 2197.8 19644 1 2962.3 18483 0 3047 18079 0 3032.6 19178 0 3504.4 18391 0 3801.1 18441 0 3857.6 18584 0 3674.4 20108 0 3721 20148 0 3844.5 19394 0 4116.7 17745 0 4105.2 17696 0 4435.2 17032 0 4296.5 16438 0 4202.5 15683 0 4562.8 15594 0 4621.4 15713 0 4697 15937 0 4591.3 16171 0 4357 15928 0 4502.6 16348 0 4443.9 15579 0 4290.9 15305 0 4199.8 15648 0 4138.5 14954 0 3970.1 15137 0 3862.3 15839 0 3701.6 16050 0 3570.12 15168 0 3801.06 17064 0 3895.51 16005 0 3917.96 14886 0 3813.06 14931 0 3667.03 14544 0 3494.17 13812 0 3364 13031 0 3295.3 12 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 6 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Bel20[t] = + 4613.22177600925 -0.061195865360757Goudprijs[t] -1244.06723492356Crisis[t] + 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) 4613.22177600925 228.323382 20.2048 0 0 Goudprijs -0.061195865360757 0.011986 -5.1058 3e-06 1e-06 Crisis -1244.06723492356 171.671535 -7.2468 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.776409600774211 R-squared 0.60281186817437 Adjusted R-squared 0.590955506030321 F-TEST (value) 50.8429028103661 F-TEST (DF numerator) 2 F-TEST (DF denominator) 67 p-value 3.68594044175552e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 542.797901831124 Sum Squared Residuals 19740180.6695622 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2649.2 2711.43786819299 -62.2378681929893 2 2579.4 2698.21956127507 -118.819561275075 3 2504.6 2762.90359096140 -258.303590961396 4 2462.3 2767.55447672881 -305.254476728812 5 2467.4 2628.27268716773 -160.872687167729 6 2446.7 2734.81468876081 -288.114688760807 7 2656.3 2928.80558195441 -272.505581954407 8 2626.2 2995.57027106299 -369.370271062993 9 2482.6 3038.10139748872 -555.501397488719 10 2539.9 3070.84118545672 -530.941185456724 11 2502.7 3084.73264689362 -582.032646893616 12 2466.9 3124.93833043563 -658.038330435633 13 2513.2 1981.23231470371 531.967685296285 14 2443.3 2019.66331815027 423.63668184973 15 2293.4 2055.46289938631 237.937100613687 16 2070.8 2060.60335207662 10.1966479233834 17 2029.6 2036.49218112448 -6.89218112447866 18 2052 2027.25160545500 24.7483945449957 19 1864.4 2034.22793410613 -169.827934106130 20 1670.1 1965.504977306 -295.404977306000 21 1811 1925.23809789862 -114.238097898622 22 1905.4 2094.44466562112 -189.044665621115 23 1862.8 2166.65578674681 -303.855786746809 24 2014.5 2194.49990548595 -179.999905485953 25 2197.8 2167.02296193897 30.7770380610269 26 2962.3 3482.13859654637 -519.838596546372 27 3047 3506.86172615212 -459.861726152118 28 3032.6 3439.60747012065 -407.007470120646 29 3504.4 3487.76861615956 16.6313838404383 30 3801.1 3484.70882289152 316.391177108476 31 3857.6 3475.95781414494 381.642185855064 32 3674.4 3382.69531533514 291.704684664858 33 3721 3380.24748072071 340.752519279288 34 3844.5 3426.38916320272 418.110836797277 35 4116.7 3527.30114518261 589.398854817389 36 4105.2 3530.29974258529 574.900257414712 37 4435.2 3570.93379718483 864.266202815169 38 4296.5 3607.28414120912 689.21585879088 39 4202.5 3653.48701955649 549.012980443508 40 4562.8 3658.9334515736 903.8665484264 41 4621.4 3651.65114359567 969.74885640433 42 4697 3637.94326975486 1059.05673024514 43 4591.3 3623.62343726044 967.676562739558 44 4357 3638.49403254311 718.505967456894 45 4502.6 3612.79176909159 889.808230908412 46 4443.9 3659.85138955401 784.04861044599 47 4290.9 3676.61905666286 614.280943337142 48 4199.8 3655.62887484412 544.171125155882 49 4138.5 3698.09880540448 440.401194595516 50 3970.1 3686.89996204347 283.200037956535 51 3862.3 3643.94046456021 218.359535439786 52 3701.6 3631.02813696909 70.571863030906 53 3570.12 3685.00289021728 -114.882890217282 54 3801.06 3568.97552949329 232.084470506714 55 3895.51 3633.78195091033 261.728049089672 56 3917.96 3702.26012424902 215.699875750985 57 3813.06 3699.50631030778 113.553689692219 58 3667.03 3723.18911020239 -56.1591102023939 59 3494.17 3767.98448364647 -273.814483646468 60 3364 3815.77845449322 -451.778454493220 61 3295.3 3843.74496496309 -548.444964963085 62 3277 3881.07444283315 -604.074442833147 63 3257.2 3912.46792176322 -655.267921763216 64 3161.7 3918.89348762610 -757.193487626095 65 3097.3 3918.46511656857 -821.16511656857 66 3061.3 3958.30362491842 -897.003624918422 67 3119.3 3961.73059337862 -842.430593378625 68 3106.22 3967.23822126109 -861.018221261093 69 3080.58 3972.92943673964 -892.349436739644 70 2981.85 3976.35640519985 -994.506405199846 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.00834308216163476 0.0166861643232695 0.991656917838365 7 0.00181870479547208 0.00363740959094417 0.998181295204528 8 0.000287093837567836 0.000574187675135673 0.999712906162432 9 0.000130813154128263 0.000261626308256525 0.999869186845872 10 2.83167245240797e-05 5.66334490481594e-05 0.999971683275476 11 8.96072240673996e-06 1.79214448134799e-05 0.999991039277593 12 6.17076556960727e-06 1.23415311392145e-05 0.99999382923443 13 1.14132862579632e-06 2.28265725159264e-06 0.999998858671374 14 2.47740234932889e-07 4.95480469865778e-07 0.999999752259765 15 2.25508338966473e-07 4.51016677932945e-07 0.999999774491661 16 2.79817927913522e-06 5.59635855827044e-06 0.99999720182072 17 5.9667641580142e-06 1.19335283160284e-05 0.999994033235842 18 4.41520009213631e-06 8.83040018427263e-06 0.999995584799908 19 1.37092569093677e-05 2.74185138187354e-05 0.99998629074309 20 0.000121411950686569 0.000242823901373137 0.999878588049314 21 0.000125200404214631 0.000250400808429262 0.999874799595785 22 6.9985338021111e-05 0.000139970676042222 0.999930014661979 23 4.2335594481768e-05 8.4671188963536e-05 0.999957664405518 24 1.67333450080796e-05 3.34666900161591e-05 0.999983266654992 25 8.03100132330144e-06 1.60620026466029e-05 0.999991968998677 26 2.42468854819470e-05 4.84937709638941e-05 0.999975753114518 27 5.1792041030865e-05 0.00010358408206173 0.99994820795897 28 0.000189206032140015 0.000378412064280029 0.99981079396786 29 0.00161203656621969 0.00322407313243938 0.99838796343378 30 0.0135414358660923 0.0270828717321847 0.986458564133908 31 0.0422006853602082 0.0844013707204164 0.957799314639792 32 0.122121490978540 0.244242981957079 0.87787850902146 33 0.358427402890646 0.716854805781293 0.641572597109354 34 0.685226558933815 0.62954688213237 0.314773441066185 35 0.787378728682815 0.425242542634370 0.212621271317185 36 0.855805695311808 0.288388609376383 0.144194304688192 37 0.894947043649216 0.210105912701569 0.105052956350784 38 0.887047605471798 0.225904789056405 0.112952394528202 39 0.858847072169265 0.282305855661469 0.141152927830735 40 0.908341051217773 0.183317897564455 0.0916589487822273 41 0.953403174468308 0.0931936510633843 0.0465968255316922 42 0.985687036712341 0.0286259265753174 0.0143129632876587 43 0.993562973788406 0.0128740524231877 0.00643702621159387 44 0.993691161643892 0.0126176767122158 0.00630883835610792 45 0.996591951844382 0.00681609631123648 0.00340804815561824 46 0.999321426616543 0.00135714676691351 0.000678573383456754 47 0.999870381405835 0.000259237188329146 0.000129618594164573 48 0.999953448815001 9.31023699974298e-05 4.65511849987149e-05 49 0.999998169451667 3.66109666536169e-06 1.83054833268085e-06 50 0.999999504102624 9.91794753074006e-07 4.95897376537003e-07 51 0.999998673440692 2.65311861499514e-06 1.32655930749757e-06 52 0.99999788315778 4.23368443959049e-06 2.11684221979524e-06 53 0.999998088003702 3.82399259632428e-06 1.91199629816214e-06 54 0.999999822944024 3.54111952755872e-07 1.77055976377936e-07 55 0.999999264283637 1.47143272569921e-06 7.35716362849604e-07 56 0.999999841894848 3.16210303397375e-07 1.58105151698687e-07 57 0.999999883819624 2.32360752082446e-07 1.16180376041223e-07 58 0.999999719481291 5.61037417310413e-07 2.80518708655207e-07 59 0.99999849642362 3.00715275923046e-06 1.50357637961523e-06 60 0.999992201960986 1.55960780276960e-05 7.79803901384798e-06 61 0.999966268063373 6.74638732543073e-05 3.37319366271536e-05 62 0.999806451778569 0.000387096442862755 0.000193548221431377 63 0.999574506361485 0.000850987277029142 0.000425493638514571 64 0.997236750687917 0.00552649862416655 0.00276324931208328 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 43 0.728813559322034 NOK 5% type I error level 48 0.813559322033898 NOK 10% type I error level 50 0.847457627118644 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/10rrlt1290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/10rrlt1290542319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/1kq6z1290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/1kq6z1290542319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/2kq6z1290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/2kq6z1290542319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/3vzn21290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/3vzn21290542319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/4vzn21290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/4vzn21290542319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/5vzn21290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/5vzn21290542319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/66q451290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/66q451290542319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/7y0lq1290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/7y0lq1290542319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/8y0lq1290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/8y0lq1290542319.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/9y0lq1290542319.png (open in new window) http://www.freestatistics.org/blog/date/2010/Nov/23/t12905422476wu67pvkodn2b31/9y0lq1290542319.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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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