Actual StataMP numerical output. Log header and machine paths omitted. . * BEGIN short . regress y x, vce(robust) Linear regression Number of obs = 4,000 F(1, 3998) = 11775.50 Prob > F = 0.0000 R-squared = 0.7514 Root MSE = 1.2294 ------------------------------------------------------------------------------ | Robust y | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- x | 1.516317 .0139733 108.51 0.000 1.488921 1.543712 _cons | .9947365 .0194616 51.11 0.000 .9565808 1.032892 ------------------------------------------------------------------------------ . * END short . record biased x . * BEGIN controlled . regress y x ability, vce(robust) Linear regression Number of obs = 4,000 F(2, 3997) = 10761.78 Prob > F = 0.0000 R-squared = 0.8390 Root MSE = .98934 ------------------------------------------------------------------------------ | Robust y | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- x | .9976925 .0159362 62.61 0.000 .9664487 1.028936 ability | 1.031297 .0224454 45.95 0.000 .9872913 1.075302 _cons | .9881499 .0156569 63.11 0.000 .9574537 1.018846 ------------------------------------------------------------------------------ . * END controlled . record controlled x . generate double clean_y=1+x+rnormal() . generate double noisy_x=x+sqrt(2)*rnormal() . * BEGIN measurement . regress clean_y noisy_x, vce(robust) Linear regression Number of obs = 4,000 F(1, 3998) = 2103.91 Prob > F = 0.0000 R-squared = 0.3458 Root MSE = 1.4013 ------------------------------------------------------------------------------ | Robust clean_y | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- noisy_x | .5177554 .0112879 45.87 0.000 .4956249 .5398859 _cons | 1.045476 .0221565 47.19 0.000 1.002037 1.088915 ------------------------------------------------------------------------------ . * END measurement . record measurement noisy_x . preserve . capture program drop mcdraw . program define mcdraw, rclass 1. syntax, N(integer) 2. clear 3. set obs `n' 4. generate double a=rnormal() 5. generate double x=a+rnormal() 6. generate double y=1+x+a+rnormal() 7. quietly regress y x 8. return scalar short=_b[x] 9. quietly regress y x a 10. return scalar adjusted=_b[x] 11. end . simulate b_short=r(short) b_adjusted=r(adjusted), reps(300) seed(6030610) nodots: mcdraw, n(100) Command: mcdraw, n(100) b_short: r(short) b_adjusted: r(adjusted) . generate n=100 . tempfile small . save `small' file /var/folders/77/1vjhzbsn76q82b04p34nzxjm0000gn/T//St62962.000002 saved as .dta format . simulate b_short=r(short) b_adjusted=r(adjusted), reps(300) seed(6030611) nodots: mcdraw, n(1000) Command: mcdraw, n(1000) b_short: r(short) b_adjusted: r(adjusted) . generate n=1000 . append using `small' . export delimited using "output/meeting_06_simulation.csv", replace (file output/meeting_06_simulation.csv not found) file output/meeting_06_simulation.csv saved . restore . . postclose results . use "output/meeting_06_estimates.dta", clear . export delimited using "output/meeting_06_estimates.csv", replace (file output/meeting_06_estimates.csv not found) file output/meeting_06_estimates.csv saved . display "ECO603_MEETING_06_COMPLETE" ECO603_MEETING_06_COMPLETE Actual StataMP numerical output; machine-specific log header omitted. . * BEGIN audit . count 420 . summarize score str el income Variable | Obs Mean Std. dev. Min Max -------------+--------------------------------------------------------- score | 420 654.1565 19.05335 605.55 706.75 str | 420 19.64043 1.891812 14 25.8 el | 420 15.76816 18.28593 0 85.53972 income | 420 15.31659 7.22589 5.335 55.328 . count if el>20 118 . * END audit . * BEGIN normal_equations . regress score str Source | SS df MS Number of obs = 420 -------------+---------------------------------- F(1, 418) = 22.58 Model | 7794.11004 1 7794.11004 Prob > F = 0.0000 Residual | 144315.484 418 345.252353 R-squared = 0.0512 -------------+---------------------------------- Adj R-squared = 0.0490 Total | 152109.594 419 363.030056 Root MSE = 18.581 ------------------------------------------------------------------------------ score | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- str | -2.279808 .4798256 -4.75 0.000 -3.22298 -1.336637 _cons | 698.933 9.467491 73.82 0.000 680.3231 717.5428 ------------------------------------------------------------------------------ . predict double fitted, xb . predict double residual, residuals . generate double xu=str*residual . summarize residual xu Variable | Obs Mean Std. dev. Min Max -------------+--------------------------------------------------------- residual | 420 6.28e-14 18.55878 -47.72669 48.54041 xu | 420 1.23e-12 361.342 -954.3068 867.0591 . quietly summarize score . scalar ybar=r(mean) . generate double total_sq=(score-ybar)^2 . generate double resid_sq=residual^2 . quietly summarize total_sq, meanonly . scalar sst=r(sum) . quietly summarize resid_sq, meanonly . scalar ssr=r(sum) . display "R2 reconstructed = " %12.9f 1-ssr/sst R2 reconstructed = 0.051240095 . assert abs(1-ssr/sst-e(r2))<1e-9 . * END normal_equations . * BEGIN precision . regress score str el income Source | SS df MS Number of obs = 420 -------------+---------------------------------- F(3, 416) = 334.89 Model | 107568.862 3 35856.2873 Prob > F = 0.0000 Residual | 44540.7318 416 107.069067 R-squared = 0.7072 -------------+---------------------------------- Adj R-squared = 0.7051 Total | 152109.594 419 363.030056 Root MSE = 10.347 ------------------------------------------------------------------------------ score | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- str | -.0687755 .2769078 -0.25 0.804 -.6130885 .4755375 el | -.4882667 .0292832 -16.67 0.000 -.5458283 -.4307052 income | 1.494517 .0748334 19.97 0.000 1.347418 1.641616 _cons | 640.3155 5.774885 110.88 0.000 628.9639 651.6671 ------------------------------------------------------------------------------ . scalar full_b=_b[str] . scalar df=e(df_r) . scalar s2=e(rss)/df . display "Observations = " e(N) " parameters including intercept = " e(rank) Observations = 420 parameters including intercept = 4 . display "Residual degrees of freedom = " df " s-squared = " s2 Residual degrees of freedom = 416 s-squared = 107.06907 . estat vif Variable | VIF 1/VIF -------------+---------------------- income | 1.14 0.873929 el | 1.12 0.891206 str | 1.07 0.931159 -------------+---------------------- Mean VIF | 1.11 . * END precision . * BEGIN linear_contrast . lincom -2*str+5*el ( 1) - 2*str + 5*el = 0 ------------------------------------------------------------------------------ score | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- (1) | -2.303783 .5903562 -3.90 0.000 -3.464236 -1.14333 ------------------------------------------------------------------------------ . * END linear_contrast . quietly regress score el income . predict double ry, residuals . quietly regress str el income . predict double rx, residuals . quietly regress ry rx, noconstant . assert abs(_b[rx]-full_b)<1e-9 . * BEGIN sample_size . set seed 6032711 . generate double subset_draw=runiform() . sort subset_draw observat . regress score str el if _n<=105, vce(robust) Linear regression Number of obs = 105 F(2, 102) = 66.11 Prob > F = 0.0000 R-squared = 0.4453 Root MSE = 14.315 ------------------------------------------------------------------------------ | Robust score | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- str | -.1764537 .6896493 -0.26 0.799 -1.54437 1.191462 el | -.6936553 .0606742 -11.43 0.000 -.8140022 -.5733083 _cons | 667.5091 14.19835 47.01 0.000 639.3467 695.6715 ------------------------------------------------------------------------------ . scalar small_b=_b[str] . scalar small_se=_se[str] . regress score str el, vce(robust) Linear regression Number of obs = 420 F(2, 417) = 223.82 Prob > F = 0.0000 R-squared = 0.4264 Root MSE = 14.464 ------------------------------------------------------------------------------ | Robust score | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- str | -1.101296 .4328472 -2.54 0.011 -1.95213 -.2504616 el | -.6497768 .0310318 -20.94 0.000 -.710775 -.5887786 _cons | 686.0322 8.728224 78.60 0.000 668.8754 703.189 ------------------------------------------------------------------------------ . scalar full_se=_se[str] . display "Observed SE ratio (105 / 420) = " small_se/full_se Observed SE ratio (105 / 420) = 1.5932859 . * END sample_size . clear . set obs 1 Number of observations (_N) was 0, now 1. . generate double smaller_b=scalar(small_b) . generate double smaller_se=scalar(small_se) . generate double full_se=scalar(full_se) . export delimited using "output/overleaf_samples.csv", replace file output/overleaf_samples.csv saved . . do "setup.do" traffic . args dataset . clear . set more off . if "`dataset'"=="schools" { . use "data/caschool.dta", clear . rename testscr score . rename el_pct el . rename avginc income . assert _N==420 . assert !missing(score,str,el,income) . } . if "`dataset'"=="retail" { . use "data/retail.dta", clear . keep if !missing(sprice,income) & sprice>0 & income>0 . generate double inc10=income/10000 . generate double lp=ln(sprice) . generate double li=ln(income) . } . if "`dataset'"=="traffic" { . use "data/traffic.dta", clear . assert _N==108 . generate month_id=ym(1981,1)+_n-1 . format month_id %tm . assert year==year(dofm(month_id)) . assert t==_n . generate byte month=month(dofm(month_id)) . local m=2 . foreach flag in feb mar apr may jun jul aug sep oct nov dec { 2. assert `flag'==(month==`m') 3. local ++m 4. } . generate double logacc=ln(totacc) . generate trend=_n . tsset month_id Time variable: month_id, 1981m1 to 1989m12 Delta: 1 month . } . if "`dataset'"=="panel" { . use "data/fatality.dta", clear . generate double fatality=10000*mrall . isid state year . xtset state year . assert _N==336 . } . if "`dataset'"=="cigarettes" { . use "data/cig_ch12.dta", clear . keep if year==1995 . generate double logq=ln(packpc) . generate double logp=ln(avgprs/cpi) . generate double salestax=(taxs-tax)/cpi . generate double logincome=ln(income/(pop*cpi)) . assert _N==48 . } . if "`dataset'"=="mortgages" { . use "data/hmda_sw.dta", clear . generate byte deny=(s7==3) if !missing(s7) . generate double pir=s46/100 . assert _N==2380 . assert !missing(deny,pir) . } . end of do-file . * BEGIN dynamic . regress logacc L.logacc trend i.month unem spdlaw beltlaw wkends, vce(robust) Linear regression Number of obs = 107 F(17, 89) = 67.48 Prob > F = 0.0000 R-squared = 0.9283 Root MSE = .03179 ------------------------------------------------------------------------------ | Robust logacc | Coefficient std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- logacc | L1. | .3326578 .090942 3.66 0.000 .151958 .5133577 | trend | .0008893 .0002165 4.11 0.000 .000459 .0013195 | month | 2 | .0146527 .0228309 0.64 0.523 -.0307119 .0600172 3 | .1430085 .0236012 6.06 0.000 .0961134 .1899037 4 | .038098 .0182453 2.09 0.040 .001845 .074351 5 | .072244 .020017 3.61 0.001 .0324707 .1120174 6 | .062745 .0195854 3.20 0.002 .0238293 .1016607 7 | .0893948 .0201297 4.44 0.000 .0493976 .1293921 8 | .0938234 .0185598 5.06 0.000 .0569454 .1307013 9 | .0761947 .0175797 4.33 0.000 .0412643 .1111252 10 | .1188968 .0212805 5.59 0.000 .0766128 .1611807 11 | .0991803 .0195533 5.07 0.000 .0603283 .1380323 12 | .1255027 .020455 6.14 0.000 .084859 .1661464 | unem | -.0125834 .0036327 -3.46 0.001 -.0198015 -.0053653 spdlaw | -.0414191 .0117486 -3.53 0.001 -.0647634 -.0180748 beltlaw | .0652123 .0140206 4.65 0.000 .0373537 .0930709 wkends | .0032099 .0035067 0.92 0.362 -.0037578 .0101777 _cons | 7.018421 .9794514 7.17 0.000 5.072272 8.96457 ------------------------------------------------------------------------------ . * END dynamic . * BEGIN diagnostics . regress logacc trend i.month unem spdlaw beltlaw wkends Source | SS df MS Number of obs = 108 -------------+---------------------------------- F(16, 91) = 57.61 Model | 1.14490873 16 .071556796 Prob > F = 0.0000 Residual | .113028314 91 .001242069 R-squared = 0.9101 -------------+---------------------------------- Adj R-squared = 0.8943 Total | 1.25793704 107 .011756421 Root MSE = .03524 ------------------------------------------------------------------------------ logacc | Coefficient Std. err. t P>|t| [95% conf. interval] -------------+---------------------------------------------------------------- trend | .0011011 .0002579 4.27 0.000 .0005889 .0016133 | month | 2 | -.0338346 .0177683 -1.90 0.060 -.0691291 .0014599 3 | .076953 .0167941 4.58 0.000 .0435936 .1103123 4 | .0104562 .0170469 0.61 0.541 -.0234053 .0443178 5 | .0237075 .0169389 1.40 0.165 -.0099395 .0573546 6 | .0219334 .0172149 1.27 0.206 -.0122619 .0561288 7 | .0499292 .0167036 2.99 0.004 .0167496 .0831088 8 | .0559523 .0168173 3.33 0.001 .0225468 .0893578 9 | .0420694 .0172819 2.43 0.017 .0077411 .0763977 10 | .081717 .0169554 4.82 0.000 .0480372 .1153968 11 | .0768721 .0172455 4.46 0.000 .0426161 .1111282 12 | .0990862 .0170705 5.80 0.000 .0651778 .1329947 | unem | -.0212173 .0033974 -6.25 0.000 -.0279659 -.0144688 spdlaw | -.0537593 .0126036 -4.27 0.000 -.0787948 -.0287238 beltlaw | .0954526 .014235 6.71 0.000 .0671765 .1237288 wkends | .0033333 .0037761 0.88 0.380 -.0041675 .0108342 _cons | 10.63986 .0630859 168.66 0.000 10.51455 10.76518 ------------------------------------------------------------------------------ . estat dwatson Durbin–Watson d-statistic( 17, 108) = 1.200475 . estat bgodfrey, lags(1 4) Breusch–Godfrey LM test for autocorrelation --------------------------------------------------------------------------- lags(p) | chi2 df Prob > chi2 -------------+------------------------------------------------------------- 1 | 13.010 1 0.0003 4 | 16.907 4 0.0020 --------------------------------------------------------------------------- H0: no serial correlation . * END diagnostics . * BEGIN arch . estat archlm, lags(1) LM test for autoregressive conditional heteroskedasticity (ARCH) --------------------------------------------------------------------------- lags(p) | chi2 df Prob > chi2 -------------+------------------------------------------------------------- 1 | 0.350 1 0.5540 --------------------------------------------------------------------------- H0: no ARCH effects vs. H1: ARCH(p) disturbance . * END arch . display "ECO603_OVERLEAF_TECHNICAL_COMPLETE" ECO603_OVERLEAF_TECHNICAL_COMPLETE