----------------------------------------------------------------------------------- . regress profits sales Source | SS df MS Number of obs = 20 -------------+---------------------------------- F(1, 18) = 12.15 Model | 255531.039 1 255531.039 Prob > F = 0.0026 Residual | 378450.711 18 21025.0395 R-squared = 0.4031 -------------+---------------------------------- Adj R-squared = 0.3699 Total | 633981.75 19 33367.4605 Root MSE = 145 ------------------------------------------------------------------------------ profits | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 2.263828 3.49 0.003 3.136051 12.6483 _cons | 116.1593 50.85614 2.28 0.035 9.314527 223.0041 ------------------------------------------------------------------------------ . estat imtest, white White's test H0: Homoskedasticity Ha: Unrestricted heteroskedasticity chi2(2) = 16.81 Prob > chi2 = 0.0002 Cameron & Trivedi's decomposition of IM-test -------------------------------------------------- Source | chi2 df p ---------------------+---------------------------- Heteroskedasticity | 16.81 2 0.0002 Skewness | 6.70 1 0.0096 Kurtosis | 0.50 1 0.4809 ---------------------+---------------------------- Total | 24.00 4 0.0001 -------------------------------------------------- . rvpplot sales . regress profits sales, vce(robust) Linear regression Number of obs = 20 F(1, 18) = 3.71 Prob > F = 0.0700 R-squared = 0.4031 Root MSE = 145 ------------------------------------------------------------------------------ | Robust profits | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 4.097213 1.93 0.070 -.7157479 16.5001 _cons | 116.1593 50.6384 2.29 0.034 9.771982 222.5467 ------------------------------------------------------------------------------ . regress profits sales, vce(bootstrap, seed(100) dots(1)) (running regress on estimation sample) Bootstrap replications (50): .........10.........20.........30.........40.........5 > 0 done Linear regression Number of obs = 20 Replications = 50 Wald chi2(1) = 3.19 Prob > chi2 = 0.0742 R-squared = 0.4031 Adj R-squared = 0.3699 Root MSE = 145.0001 ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based profits | coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 4.421186 1.79 0.074 -.77319 16.55754 _cons | 116.1593 55.13846 2.11 0.035 8.089926 224.2287 ------------------------------------------------------------------------------ . regress profits sales, vce(bootstrap, seed(200) dots(1)) (running regress on estimation sample) Bootstrap replications (50): .........10.........20.........30.........40.........5 > 0 done Linear regression Number of obs = 20 Replications = 50 Wald chi2(1) = 3.97 Prob > chi2 = 0.0462 R-squared = 0.4031 Adj R-squared = 0.3699 Root MSE = 145.0001 ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based profits | coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 3.959396 1.99 0.046 .1319031 15.65245 _cons | 116.1593 56.92895 2.04 0.041 4.580618 227.738 ------------------------------------------------------------------------------ . regress profits sales, vce(bootstrap, seed(300) dots(1)) (running regress on estimation sample) Bootstrap replications (50): .........10.........20.........30.........40.........5 > 0 done Linear regression Number of obs = 20 Replications = 50 Wald chi2(1) = 3.03 Prob > chi2 = 0.0817 R-squared = 0.4031 Adj R-squared = 0.3699 Root MSE = 145.0001 ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based profits | coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 4.533274 1.74 0.082 -.992877 16.77723 _cons | 116.1593 58.18343 2.00 0.046 2.121884 230.1968 ------------------------------------------------------------------------------ . regress profits sales, vce(bootstrap, reps(200) seed(100) dots(1)) (running regress on estimation sample) Bootstrap replications (200): .........10.........20.........30.........40......... > 50.........60.........70.........80.........90.........100.........110.........12 > 0.........130.........140.........150.........160.........170.........180........ > .190.........200 done Linear regression Number of obs = 20 Replications = 200 Wald chi2(1) = 3.79 Prob > chi2 = 0.0514 R-squared = 0.4031 Adj R-squared = 0.3699 Root MSE = 145.0001 ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based profits | coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 4.051287 1.95 0.051 -.0482007 15.83255 _cons | 116.1593 54.33127 2.14 0.033 9.671981 222.6467 ------------------------------------------------------------------------------ . regress profits sales, vce(bootstrap, reps(200) seed(200) dots(1)) (running regress on estimation sample) Bootstrap replications (200): .........10.........20.........30.........40......... > 50.........60.........70.........80.........90.........100.........110.........12 > 0.........130.........140.........150.........160.........170.........180........ > .190.........200 done Linear regression Number of obs = 20 Replications = 200 Wald chi2(1) = 3.79 Prob > chi2 = 0.0515 R-squared = 0.4031 Adj R-squared = 0.3699 Root MSE = 145.0001 ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based profits | coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 4.053601 1.95 0.052 -.0527361 15.83709 _cons | 116.1593 54.82845 2.12 0.034 8.697529 223.6211 ------------------------------------------------------------------------------ . regress profits sales, vce(bootstrap, reps(200) seed(300) dots(1)) (running regress on estimation sample) Bootstrap replications (200): .........10.........20.........30.........40......... > 50.........60.........70.........80.........90.........100.........110.........12 > 0.........130.........140.........150.........160.........170.........180........ > .190.........200 done Linear regression Number of obs = 20 Replications = 200 Wald chi2(1) = 3.59 Prob > chi2 = 0.0583 R-squared = 0.4031 Adj R-squared = 0.3699 Root MSE = 145.0001 ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based profits | coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 4.167655 1.89 0.058 -.2762776 16.06063 _cons | 116.1593 54.60675 2.13 0.033 9.132046 223.1866 ------------------------------------------------------------------------------ . regress profits sales, vce(bootstrap, reps(500) seed(100) dots(1)) (running regress on estimation sample) Bootstrap replications (500): .........10.........20.........30.........40......... > 50.........60.........70.........80.........90.........100.........110.........12 > 0.........130.........140.........150.........160.........170.........180........ > .190.........200.........210.........220.........230.........240.........250..... > ....260.........270.........280.........290.........300.........310.........320.. > .......330.........340.........350.........360.........370.........380.........39 > 0.........400.........410.........420.........430.........440.........450........ > .460.........470.........480.........490.........500 done Linear regression Number of obs = 20 Replications = 500 Wald chi2(1) = 3.56 Prob > chi2 = 0.0593 R-squared = 0.4031 Adj R-squared = 0.3699 Root MSE = 145.0001 ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based profits | coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 4.184801 1.89 0.059 -.3098835 16.09424 _cons | 116.1593 54.94236 2.11 0.034 8.474269 223.8444 ------------------------------------------------------------------------------ . regress profits sales, vce(bootstrap, reps(500) seed(200) dots(1)) (running regress on estimation sample) Bootstrap replications (500): .........10.........20.........30.........40......... > 50.........60.........70.........80.........90.........100.........110.........12 > 0.........130.........140.........150.........160.........170.........180........ > .190.........200.........210.........220.........230.........240.........250..... > ....260.........270.........280.........290.........300.........310.........320.. > .......330.........340.........350.........360.........370.........380.........39 > 0.........400.........410.........420.........430.........440.........450........ > .460.........470.........480.........490.........500 done Linear regression Number of obs = 20 Replications = 500 Wald chi2(1) = 3.66 Prob > chi2 = 0.0558 R-squared = 0.4031 Adj R-squared = 0.3699 Root MSE = 145.0001 ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based profits | coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 4.126129 1.91 0.056 -.1948875 15.97924 _cons | 116.1593 53.9026 2.15 0.031 10.51217 221.8065 ------------------------------------------------------------------------------ . regress profits sales, vce(bootstrap, reps(500) seed(300) dots(1)) (running regress on estimation sample) Bootstrap replications (500): .........10.........20.........30.........40......... > 50.........60.........70.........80.........90.........100.........110.........12 > 0.........130.........140.........150.........160.........170.........180........ > .190.........200.........210.........220.........230.........240.........250..... > ....260.........270.........280.........290.........300.........310.........320.. > .......330.........340.........350.........360.........370.........380.........39 > 0.........400.........410.........420.........430.........440.........450........ > .460.........470.........480.........490.........500 done Linear regression Number of obs = 20 Replications = 500 Wald chi2(1) = 3.58 Prob > chi2 = 0.0585 R-squared = 0.4031 Adj R-squared = 0.3699 Root MSE = 145.0001 ------------------------------------------------------------------------------ | Observed Bootstrap Normal-based profits | coefficient std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- sales | 7.892176 4.171369 1.89 0.058 -.2835558 16.06791 _cons | 116.1593 54.8983 2.12 0.034 8.560619 223.758 ------------------------------------------------------------------------------ . hetregress profits sales, twostep het(sales ) Heteroskedastic linear regression Number of obs = 20 Two-step GLS estimation Wald chi2(1) = 3.83 Prob > chi2 = 0.0503 ------------------------------------------------------------------------------ profits | Coefficient Std. err. z P>|z| [95% conf. interval] -------------+---------------------------------------------------------------- profits | sales | 4.922191 2.514533 1.96 0.050 -.0062025 9.850584 _cons | 133.8419 22.65399 5.91 0.000 89.44087 178.2429 -------------+---------------------------------------------------------------- lnsigma2 | sales | .1207585 .0346824 3.48 0.000 .0527822 .1887349 _cons | 7.227046 .7791297 9.28 0.000 5.69998 8.754112 ------------------------------------------------------------------------------ Wald test of lnsigma2=0: chi2(1) = 12.12 Prob > chi2 = 0.0005 . log close name: log: E:\Work\ako\Finantsökonomeetria\Teemad\7 Heteroskedastiivsus, instrume > ndid\Harjutused\Ülesanne 2.log log type: text closed on: 27 Mar 2025, 22:11:15 -----------------------------------------------------------------------------------