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| From | "Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk> |
| To | <statalist@hsphsun2.harvard.edu> |
| Subject | RE: st: RE: ivreg2 vs. Manual IV |
| Date | Mon, 7 Mar 2011 20:05:08 -0000 |
Erkal, > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Erkal Ersoy > Sent: 07 March 2011 19:16 > To: statalist@hsphsun2.harvard.edu > Subject: Re: st: RE: ivreg2 vs. Manual IV > > Professor Schaffer, > > Thank you for your quick response. I am using Stata 11.1 and > ivreg2's version 3.0.06, 30Jan2011. > > 1. I tried the partial option and only the R^2 terms > change--all coefficients and z-stats stay the same (output below). > > 2. "ivregress 2sls loghrearn age agesq YR* (educ=QTR*), > robust" gave the same output as ivreg2 (none of the weak ID > and Sargan stats, of > course) > > "ivreg loghrearn age agesq YR* (educ=QTR*), robust" gives > almost the same output as the one I get doing the 1st and 2nd > stage regressions manually. The coefficients are the same on > educ, age and agesq. But with -ivreg-, educ, age and agesq > are all significant at the 5% level--using the manual way, > agesq was not significant. But when you do 2SLS "manually", the SEs in the 2nd stage regression are wrong. Are you taking account of this fact? --Mark > 3. When I do "ivreg2 loghrearn age agesq YR* (educ=yhat), > robust" I get the same output as "ivreg2 loghrearn age agesq > YR* (educ=QTR*), robust" > > With "ivregress 2sls loghrearn age agesq YR* (educ=yhat), > robust" I get the same output as "ivreg2 loghrearn age agesq > YR* (educ=QTR*), robust" > > Lastly, with "ivreg loghrearn age agesq YR* (educ=yhat), > robust" I get the same output as "ivreg loghrearn age agesq > YR* (educ=QTR*), robust" > > > I am still confused as to which approach I should be using to > get as robust estimates as possible. Which one would you recommend? > > Best, > Erkal > > > Output: > > . ivreg2 loghrearn age agesq YR* (educ=QTR*), robust > partial(YR*) Warning - collinearities detected > Vars dropped: YR57 YR58 > > IV (2SLS) estimation > -------------------- > > Estimates efficient for homoskedasticity only Statistics > robust to heteroskedasticity > > Number > of obs = 1930 > F( 3, > 1899) = 10.81 > Prob > F > = 0.0000 > Total (centered) SS = 400.8160242 > Centered R2 = 0.2049 > Total (uncentered) SS = 400.8160242 > Uncentered R2 = 0.2049 > Residual SS = 318.6886262 Root > MSE = .4064 > > -------------------------------------------------------------- > ---------------- > | Robust > loghrearn | Coef. Std. Err. z P>|z| [95% > Conf. Interval] > -------------+------------------------------------------------ > ---------- > -------------+------ > educ | .0775439 .0160336 4.84 0.000 > .0461186 .1089692 > age | .0960862 .1423336 0.68 0.500 > -.1828825 .3750549 > agesq | -.0010696 .0017911 -0.60 0.550 > -.0045801 .0024409 > -------------------------------------------------------------- > ---------------- > Underidentification test (Kleibergen-Paap rk LM statistic): > 87.887 > Chi-sq(87) > P-val = 0.4532 > -------------------------------------------------------------- > ---------------- > Weak identification test (Cragg-Donald Wald F statistic): > 1.147 > (Kleibergen-Paap rk Wald F > statistic): 1.139 > Stock-Yogo weak ID test critical values: 5% maximal IV > relative bias 21.12 > 10% maximal IV > relative bias 10.91 > 20% maximal IV > relative bias 5.69 > 30% maximal IV > relative bias 3.92 > 10% maximal IV size > 222.24 > 15% maximal IV size > 113.33 > 20% maximal IV size > 76.67 > 25% maximal IV size > 58.36 > Source: Stock-Yogo (2005). Reproduced by permission. > NB: Critical values are for Cragg-Donald F statistic and > i.i.d. errors. > -------------------------------------------------------------- > ---------------- > Hansen J statistic (overidentification test of all > instruments): 82.395 > Chi-sq(86) > P-val = 0.5901 > -------------------------------------------------------------- > ---------------- > Instrumented: educ > Included instruments: age agesq > Excluded instruments: QTR230 QTR231 QTR232 QTR233 QTR234 > QTR235 QTR236 QTR237 > QTR238 QTR239 QTR240 QTR241 QTR242 > QTR243 QTR244 QTR245 > QTR246 QTR247 QTR248 QTR249 QTR250 > QTR251 QTR252 QTR253 > QTR254 QTR255 QTR256 QTR257 QTR258 > QTR330 QTR331 QTR332 > QTR333 QTR334 QTR335 QTR336 QTR337 > QTR338 QTR339 QTR340 > QTR341 QTR342 QTR343 QTR344 QTR345 > QTR346 QTR347 QTR348 > QTR349 QTR350 QTR351 QTR352 QTR353 > QTR354 QTR355 QTR356 > QTR357 QTR358 QTR430 QTR431 QTR432 > QTR433 QTR434 QTR435 > QTR436 QTR437 QTR438 QTR439 QTR440 > QTR441 QTR442 QTR443 > QTR444 QTR445 QTR446 QTR447 QTR448 > QTR449 QTR450 QTR451 > QTR452 QTR453 QTR454 QTR455 QTR456 QTR457 QTR458 > Partialled-out: YR30 YR31 YR32 YR33 YR34 YR35 YR36 YR37 > YR38 YR39 YR40 > YR41 YR42 YR43 YR44 YR45 YR46 YR47 YR48 > YR49 YR50 YR51 > YR52 YR53 YR54 YR55 YR56 _cons > nb: small-sample adjustments account for > partialled-out variables > Dropped collinear: YR57 YR58 > -------------------------------------------------------------- > ---------------- > > . ivregress 2sls loghrearn age agesq YR* (educ=QTR*), robust > note: YR57 omitted because of collinearity > note: YR58 omitted because of collinearity > > Instrumental variables (2SLS) regression Number > of obs = 1930 > Wald > chi2(30) = 312.53 > Prob > > chi2 = 0.0000 > > R-squared = 0.2791 > Root > MSE = .40635 > > -------------------------------------------------------------- > ---------------- > | Robust > loghrearn | Coef. Std. Err. z P>|z| [95% > Conf. Interval] > -------------+------------------------------------------------ > ---------- > -------------+------ > educ | .0775439 .0160336 4.84 0.000 > .0461186 .1089692 > age | .0960863 .1423337 0.68 0.500 > -.1828826 .3750552 > agesq | -.0010696 .0017911 -0.60 0.550 > -.0045801 .0024409 > YR30 | .0729702 .1068258 0.68 0.495 > -.1364044 .2823448 > YR31 | .0770476 .1309373 0.59 0.556 > -.1795848 .3336799 > YR32 | .0973404 .1634284 0.60 0.551 > -.2229734 .4176542 > YR33 | .141803 .1929298 0.73 0.462 > -.2363325 .5199385 > YR34 | -.1038156 .2259734 -0.46 0.646 > -.5467153 .3390841 > YR35 | -.0904715 .2649789 -0.34 0.733 > -.6098205 .4288776 > YR36 | .0211239 .2888762 0.07 0.942 > -.5450631 .5873109 > YR37 | -.0095477 .309436 -0.03 0.975 > -.6160311 .5969358 > YR38 | -.1243089 .3284458 -0.38 0.705 > -.7680508 .519433 > YR39 | .031897 .3397094 0.09 0.925 > -.6339212 .6977152 > YR40 | -.0124043 .3501857 -0.04 0.972 > -.6987556 .673947 > YR41 | -.0104207 .3627671 -0.03 0.977 > -.7214312 .7005898 > YR42 | -.0702436 .3685106 -0.19 0.849 > -.7925111 .652024 > YR43 | .0575309 .374727 0.15 0.878 > -.6769206 .7919823 > YR44 | .0096373 .3702528 0.03 0.979 > -.7160449 .7353195 > YR45 | .0027419 .3662022 0.01 0.994 > -.7150012 .7204851 > YR46 | -.0634878 .3580731 -0.18 0.859 > -.7652981 .6383225 > YR47 | -.0433442 .3435527 -0.13 0.900 > -.716695 .6300067 > YR48 | .0054649 .3285693 0.02 0.987 > -.6385191 .6494489 > YR49 | -.0997214 .3100031 -0.32 0.748 > -.7073162 .5078735 > YR50 | -.0773026 .2898275 -0.27 0.790 > -.645354 .4907489 > YR51 | .0145725 .2641776 0.06 0.956 > -.5032062 .5323511 > YR52 | -.0292526 .2321381 -0.13 0.900 > -.484235 .4257298 > YR53 | .0184267 .1987075 0.09 0.926 > -.3710328 .4078863 > YR54 | -.0401415 .1639573 -0.24 0.807 > -.3614919 .2812088 > YR55 | -.0174243 .1241563 -0.14 0.888 > -.2607661 .2259175 > YR56 | -.0569665 .0846968 -0.67 0.501 > -.2229692 .1090362 > YR57 | (omitted) > YR58 | (omitted) > _cons | -.7113387 2.494937 -0.29 0.776 > -5.601325 4.178648 > -------------------------------------------------------------- > ---------------- > Instrumented: educ > Instruments: age agesq YR30 YR31 YR32 YR33 YR34 YR35 YR36 > YR37 YR38 YR39 > YR40 YR41 YR42 YR43 YR44 YR45 YR46 YR47 YR48 > YR49 YR50 YR51 > YR52 YR53 YR54 YR55 YR56 QTR230 QTR231 QTR232 > QTR233 QTR234 > QTR235 QTR236 QTR237 QTR238 QTR239 QTR240 QTR241 QTR242 > QTR243 QTR244 QTR245 QTR246 QTR247 QTR248 QTR249 QTR250 > QTR251 QTR252 QTR253 QTR254 QTR255 QTR256 QTR257 QTR258 > QTR330 QTR331 QTR332 QTR333 QTR334 QTR335 QTR336 QTR337 > QTR338 QTR339 QTR340 QTR341 QTR342 QTR343 QTR344 QTR345 > QTR346 QTR347 QTR348 QTR349 QTR350 QTR351 QTR352 QTR353 > QTR354 QTR355 QTR356 QTR357 QTR358 QTR430 QTR431 QTR432 > QTR433 QTR434 QTR435 QTR436 QTR437 QTR438 QTR439 QTR440 > QTR441 QTR442 QTR443 QTR444 QTR445 QTR446 QTR447 QTR448 > QTR449 QTR450 QTR451 QTR452 QTR453 QTR454 QTR455 QTR456 > QTR457 QTR458 > > . ivreg loghrearn age agesq YR* (educ=QTR*), robust > > Instrumental variables (2SLS) regression Number > of obs = 1930 > F( 30, > 1899) = 10.25 > Prob > > F = 0.0000 > > R-squared = 0.2791 > Root > MSE = .40966 > > -------------------------------------------------------------- > ---------------- > | Robust > loghrearn | Coef. Std. Err. t P>|t| [95% > Conf. Interval] > -------------+------------------------------------------------ > ---------- > -------------+------ > educ | .0775439 .0161639 4.80 0.000 > .045843 .1092449 > age | .0694072 .0275518 2.52 0.012 > .0153722 .1234423 > agesq | -.0007319 .0003619 -2.02 0.043 > -.0014417 -.0000221 > YR30 | .0824261 .0942624 0.87 0.382 > -.1024427 .2672948 > YR31 | .0952839 .087805 1.09 0.278 > -.0769205 .2674882 > YR32 | .1236817 .0844014 1.47 0.143 > -.0418475 .2892109 > YR33 | .175574 .0761246 2.31 0.021 > .0262772 .3248707 > YR34 | -.0632905 .082945 -0.76 0.446 > -.2259633 .0993823 > YR35 | -.0438676 .1183119 -0.37 0.711 > -.2759026 .1881675 > YR36 | .0731311 .1048125 0.70 0.485 > -.1324285 .2786908 > YR37 | .0471875 .0985247 0.48 0.632 > -.1460405 .2404155 > YR38 | -.0635212 .0990568 -0.64 0.521 > -.2577928 .1307504 > YR39 | .0960618 .0859788 1.12 0.264 > -.072561 .2646846 > YR40 | .0544622 .0782422 0.70 0.486 > -.0989875 .2079118 > YR41 | .058472 .085802 0.68 0.496 > -.1098041 .2267481 > YR42 | (omitted) > YR43 | .1284498 .1083614 1.19 0.236 > -.08407 .3409697 > YR44 | .0805562 .0827555 0.97 0.330 > -.081745 .2428575 > YR45 | .0729855 .0897922 0.81 0.416 > -.1031163 .2490873 > YR46 | .005405 .0832444 0.06 0.948 > -.1578551 .168665 > YR47 | .0235223 .0713527 0.33 0.742 > -.1164156 .1634602 > YR48 | .0696297 .0677743 1.03 0.304 > -.0632903 .2025496 > YR49 | -.0389337 .06661 -0.58 0.559 > -.1695702 .0917028 > YR50 | -.0205674 .0725417 -0.28 0.777 > -.1628372 .1217024 > YR51 | .0665797 .0719262 0.93 0.355 > -.0744829 .2076424 > YR52 | .0173513 .0595863 0.29 0.771 > -.0995101 .1342126 > YR53 | .0589519 .0551962 1.07 0.286 > -.0492997 .1672034 > YR54 | -.0063706 .0538644 -0.12 0.906 > -.1120102 .099269 > YR55 | .008917 .0516867 0.17 0.863 > -.0924517 .1102857 > YR56 | -.0387302 .0481101 -0.81 0.421 > -.1330844 .055624 > YR57 | .0094559 .0500105 0.19 0.850 > -.0886254 .1075371 > YR58 | (omitted) > _cons | -.255431 .4852748 -0.53 0.599 > -1.207159 .6962967 > -------------------------------------------------------------- > ---------------- > Instrumented: educ > Instruments: age agesq YR30 YR31 YR32 YR33 YR34 YR35 YR36 > YR37 YR38 YR39 > YR40 YR41 YR42 YR43 YR44 YR45 YR46 YR47 YR48 > YR49 YR50 YR51 > YR52 YR53 YR54 YR55 YR56 YR57 YR58 QTR230 QTR231 QTR232 > QTR233 QTR234 QTR235 QTR236 QTR237 QTR238 QTR239 QTR240 > QTR241 QTR242 QTR243 QTR244 QTR245 QTR246 QTR247 QTR248 > QTR249 QTR250 QTR251 QTR252 QTR253 QTR254 QTR255 QTR256 > QTR257 QTR258 QTR330 QTR331 QTR332 QTR333 QTR334 QTR335 > QTR336 QTR337 QTR338 QTR339 QTR340 QTR341 QTR342 QTR343 > QTR344 QTR345 QTR346 QTR347 QTR348 QTR349 QTR350 QTR351 > QTR352 QTR353 QTR354 QTR355 QTR356 QTR357 QTR358 QTR430 > QTR431 QTR432 QTR433 QTR434 QTR435 QTR436 QTR437 QTR438 > QTR439 QTR440 QTR441 QTR442 QTR443 QTR444 QTR445 QTR446 > QTR447 QTR448 QTR449 QTR450 QTR451 QTR452 QTR453 QTR454 > QTR455 QTR456 QTR457 QTR458 > -------------------------------------------------------------- > ---------------- > > > . ivreg2 loghrearn age agesq YR* (educ=yhat), robust Warning > - collinearities detected > Vars dropped: YR57 YR58 > > IV (2SLS) estimation > -------------------- > > Estimates efficient for homoskedasticity only Statistics > robust to heteroskedasticity > > Number > of obs = 1930 > F( 30, > 1899) = 10.25 > Prob > F > = 0.0000 > Total (centered) SS = 442.0938306 > Centered R2 = 0.2791 > Total (uncentered) SS = 10400.84512 > Uncentered R2 = 0.9694 > Residual SS = 318.6886296 Root > MSE = .4064 > > -------------------------------------------------------------- > ---------------- > | Robust > loghrearn | Coef. Std. Err. z P>|z| [95% > Conf. Interval] > -------------+------------------------------------------------ > ---------- > -------------+------ > educ | .0775439 .0160336 4.84 0.000 > .0461186 .1089692 > age | .0960844 .1423327 0.68 0.500 > -.1828826 .3750515 > agesq | -.0010696 .0017911 -0.60 0.550 > -.0045801 .0024409 > YR30 | .0729708 .1068256 0.68 0.495 > -.1364036 .2823452 > YR31 | .0770488 .1309368 0.59 0.556 > -.1795827 .3336803 > YR32 | .0973422 .1634277 0.60 0.551 > -.2229702 .4176545 > YR33 | .1418053 .1929288 0.74 0.462 > -.2363282 .5199388 > YR34 | -.1038129 .2259721 -0.46 0.646 > -.5467101 .3390843 > YR35 | -.0904684 .2649774 -0.34 0.733 > -.6098146 .4288779 > YR36 | .0211273 .2888746 0.07 0.942 > -.5450564 .5873111 > YR37 | -.0095438 .3094342 -0.03 0.975 > -.6160237 .596936 > YR38 | -.1243048 .3284438 -0.38 0.705 > -.7680429 .5194333 > YR39 | .0319013 .3397073 0.09 0.925 > -.6339128 .6977154 > YR40 | -.0123998 .3501835 -0.04 0.972 > -.6987468 .6739472 > YR41 | -.0104161 .3627649 -0.03 0.977 > -.7214222 .70059 > YR42 | -.0702389 .3685083 -0.19 0.849 > -.7925019 .6520242 > YR43 | .0575356 .3747247 0.15 0.878 > -.6769114 .7919825 > YR44 | .009642 .3702505 0.03 0.979 > -.7160356 .7353196 > YR45 | .0027466 .3661999 0.01 0.994 > -.7149921 .7204852 > YR46 | -.0634832 .3580708 -0.18 0.859 > -.7652891 .6383227 > YR47 | -.0433398 .3435505 -0.13 0.900 > -.7166864 .6300068 > YR48 | .0054691 .3285672 0.02 0.987 > -.6385108 .649449 > YR49 | -.0997174 .3100011 -0.32 0.748 > -.7073084 .5078736 > YR50 | -.0772989 .2898257 -0.27 0.790 > -.6453468 .490749 > YR51 | .0145758 .264176 0.06 0.956 > -.5031997 .5323513 > YR52 | -.0292497 .2321367 -0.13 0.900 > -.4842293 .4257298 > YR53 | .0184292 .1987063 0.09 0.926 > -.3710279 .4078863 > YR54 | -.0401395 .1639563 -0.24 0.807 > -.3614879 .2812089 > YR55 | -.0174228 .1241556 -0.14 0.888 > -.2607633 .2259176 > YR56 | -.0569656 .0846964 -0.67 0.501 > -.2229675 .1090363 > _cons | -.7113064 2.494921 -0.29 0.776 > -5.601261 4.178649 > -------------------------------------------------------------- > ---------------- > Underidentification test (Kleibergen-Paap rk LM statistic): > 73.415 > Chi-sq(1) > P-val = 0.0000 > -------------------------------------------------------------- > ---------------- > Weak identification test (Cragg-Donald Wald F statistic): > 104.504 > (Kleibergen-Paap rk Wald F > statistic): 86.424 > Stock-Yogo weak ID test critical values: 10% maximal IV size > 16.38 > 15% maximal IV size > 8.96 > 20% maximal IV size > 6.66 > 25% maximal IV size > 5.53 > Source: Stock-Yogo (2005). Reproduced by permission. > NB: Critical values are for Cragg-Donald F statistic and > i.i.d. errors. > -------------------------------------------------------------- > ---------------- > Hansen J statistic (overidentification test of all > instruments): 0.000 > (equation > exactly identified) > -------------------------------------------------------------- > ---------------- > Instrumented: educ > Included instruments: age agesq YR30 YR31 YR32 YR33 YR34 YR35 > YR36 YR37 YR38 > YR39 YR40 YR41 YR42 YR43 YR44 YR45 YR46 > YR47 YR48 YR49 > YR50 YR51 YR52 YR53 YR54 YR55 YR56 > Excluded instruments: yhat > Dropped collinear: YR57 YR58 > -------------------------------------------------------------- > ---------------- > > . ivregress 2sls loghrearn age agesq YR* (educ=yhat), robust > note: YR57 omitted because of collinearity > note: YR58 omitted because of collinearity > > Instrumental variables (2SLS) regression Number > of obs = 1930 > Wald > chi2(30) = 312.53 > Prob > > chi2 = 0.0000 > > R-squared = 0.2791 > Root > MSE = .40635 > > -------------------------------------------------------------- > ---------------- > | Robust > loghrearn | Coef. Std. Err. z P>|z| [95% > Conf. Interval] > -------------+------------------------------------------------ > ---------- > -------------+------ > educ | .0775439 .0160336 4.84 0.000 > .0461186 .1089692 > age | .0960863 .1423337 0.68 0.500 > -.1828826 .3750552 > agesq | -.0010696 .0017911 -0.60 0.550 > -.0045801 .0024409 > YR30 | .0729702 .1068258 0.68 0.495 > -.1364044 .2823448 > YR31 | .0770476 .1309373 0.59 0.556 > -.1795848 .3336799 > YR32 | .0973404 .1634284 0.60 0.551 > -.2229734 .4176542 > YR33 | .141803 .1929298 0.73 0.462 > -.2363325 .5199385 > YR34 | -.1038156 .2259734 -0.46 0.646 > -.5467153 .3390841 > YR35 | -.0904715 .2649789 -0.34 0.733 > -.6098205 .4288776 > YR36 | .0211239 .2888762 0.07 0.942 > -.5450631 .5873109 > YR37 | -.0095477 .309436 -0.03 0.975 > -.6160311 .5969358 > YR38 | -.1243089 .3284458 -0.38 0.705 > -.7680508 .519433 > YR39 | .031897 .3397094 0.09 0.925 > -.6339212 .6977152 > YR40 | -.0124043 .3501857 -0.04 0.972 > -.6987556 .673947 > YR41 | -.0104207 .3627671 -0.03 0.977 > -.7214312 .7005898 > YR42 | -.0702436 .3685106 -0.19 0.849 > -.7925111 .652024 > YR43 | .0575309 .374727 0.15 0.878 > -.6769206 .7919823 > YR44 | .0096373 .3702528 0.03 0.979 > -.7160449 .7353194 > YR45 | .0027419 .3662022 0.01 0.994 > -.7150012 .7204851 > YR46 | -.0634878 .3580731 -0.18 0.859 > -.7652981 .6383225 > YR47 | -.0433442 .3435527 -0.13 0.900 > -.716695 .6300067 > YR48 | .0054649 .3285693 0.02 0.987 > -.6385191 .6494489 > YR49 | -.0997214 .3100031 -0.32 0.748 > -.7073162 .5078735 > YR50 | -.0773026 .2898275 -0.27 0.790 > -.6453541 .4907489 > YR51 | .0145725 .2641776 0.06 0.956 > -.5032062 .5323511 > YR52 | -.0292526 .2321381 -0.13 0.900 > -.484235 .4257298 > YR53 | .0184267 .1987075 0.09 0.926 > -.3710328 .4078863 > YR54 | -.0401415 .1639573 -0.24 0.807 > -.3614919 .2812088 > YR55 | -.0174243 .1241563 -0.14 0.888 > -.2607661 .2259175 > YR56 | -.0569665 .0846968 -0.67 0.501 > -.2229692 .1090362 > YR57 | (omitted) > YR58 | (omitted) > _cons | -.7113387 2.494937 -0.29 0.776 > -5.601325 4.178648 > -------------------------------------------------------------- > ---------------- > Instrumented: educ > Instruments: age agesq YR30 YR31 YR32 YR33 YR34 YR35 YR36 > YR37 YR38 YR39 > YR40 YR41 YR42 YR43 YR44 YR45 YR46 YR47 YR48 > YR49 YR50 YR51 > YR52 YR53 YR54 YR55 YR56 yhat > > . ivreg loghrearn age agesq YR* (educ=yhat), robust > > Instrumental variables (2SLS) regression Number > of obs = 1930 > F( 30, > 1899) = 10.25 > Prob > > F = 0.0000 > > R-squared = 0.2791 > Root > MSE = .40966 > > -------------------------------------------------------------- > ---------------- > | Robust > loghrearn | Coef. Std. Err. t P>|t| [95% > Conf. Interval] > -------------+------------------------------------------------ > ---------- > -------------+------ > educ | .0775439 .0161639 4.80 0.000 > .045843 .1092449 > age | .0694072 .0275518 2.52 0.012 > .0153722 .1234423 > agesq | -.0007319 .0003619 -2.02 0.043 > -.0014417 -.0000221 > YR30 | .0824261 .0942624 0.87 0.382 > -.1024427 .2672948 > YR31 | .0952839 .087805 1.09 0.278 > -.0769205 .2674882 > YR32 | .1236817 .0844014 1.47 0.143 > -.0418475 .2892109 > YR33 | .175574 .0761246 2.31 0.021 > .0262772 .3248707 > YR34 | -.0632905 .082945 -0.76 0.446 > -.2259633 .0993823 > YR35 | -.0438676 .1183119 -0.37 0.711 > -.2759026 .1881675 > YR36 | .0731311 .1048125 0.70 0.485 > -.1324285 .2786908 > YR37 | .0471875 .0985247 0.48 0.632 > -.1460405 .2404155 > YR38 | -.0635212 .0990568 -0.64 0.521 > -.2577928 .1307504 > YR39 | .0960618 .0859788 1.12 0.264 > -.072561 .2646846 > YR40 | .0544622 .0782422 0.70 0.486 > -.0989875 .2079118 > YR41 | .058472 .085802 0.68 0.496 > -.1098041 .2267481 > YR42 | (omitted) > YR43 | .1284498 .1083614 1.19 0.236 > -.08407 .3409697 > YR44 | .0805562 .0827555 0.97 0.330 > -.081745 .2428575 > YR45 | .0729855 .0897922 0.81 0.416 > -.1031163 .2490873 > YR46 | .005405 .0832444 0.06 0.948 > -.1578551 .168665 > YR47 | .0235223 .0713527 0.33 0.742 > -.1164156 .1634602 > YR48 | .0696297 .0677743 1.03 0.304 > -.0632903 .2025496 > YR49 | -.0389337 .06661 -0.58 0.559 > -.1695702 .0917028 > YR50 | -.0205674 .0725417 -0.28 0.777 > -.1628372 .1217024 > YR51 | .0665797 .0719262 0.93 0.355 > -.0744829 .2076424 > YR52 | .0173513 .0595863 0.29 0.771 > -.0995101 .1342126 > YR53 | .0589519 .0551962 1.07 0.286 > -.0492997 .1672034 > YR54 | -.0063706 .0538644 -0.12 0.906 > -.1120102 .099269 > YR55 | .008917 .0516867 0.17 0.863 > -.0924517 .1102857 > YR56 | -.0387302 .0481101 -0.81 0.421 > -.1330844 .055624 > YR57 | .0094559 .0500105 0.19 0.850 > -.0886254 .1075371 > YR58 | (omitted) > _cons | -.255431 .4852748 -0.53 0.599 > -1.207159 .6962967 > -------------------------------------------------------------- > ---------------- > Instrumented: educ > Instruments: age agesq YR30 YR31 YR32 YR33 YR34 YR35 YR36 > YR37 YR38 YR39 > YR40 YR41 YR42 YR43 YR44 YR45 YR46 YR47 YR48 > YR49 YR50 YR51 > YR52 YR53 YR54 YR55 YR56 YR57 YR58 yhat > -------------------------------------------------------------- > ---------------- > > > > > > > On Mon, Mar 7, 2011 at 10:32 AM, Schaffer, Mark E > <M.E.Schaffer@hw.ac.uk> wrote: > >> Erkal, > >> > >> You've got a lot of dummy regressors and instruments, most > of which > >> are not statistically significant, so my first guess would be > >> something to do with numerical accuracy. You should tell > us, though, > >> which versions of Stata and -ivreg2- you are using. > >> > >> Here are a few things you can experiment with: > >> > >> 1. Do your results slightly change again if you partial > out the year > >> dummies with the -partial- option? > >> > >> ivreg2 loghrearn age agesq YR* (educ=QTR*), robust partial(YR*) > >> > >> 2. There are two official IV routines in Stata, -ivregress- and > >> -ivreg-. The former is documented in Stata 11, the latter is not, > >> but its syntax is the same as that of -ivreg2-: > >> > >> ivregress 2sls loghrearn age agesq YR* (educ=QTR*), robust > >> > >> ivreg loghrearn age agesq YR* (educ=QTR*), robust > >> > >> The reason to try out -ivreg- is that it is implemented > using -regress-. > >> For that reason, it's likely to be very accurate in the face of > >> numerical challenges. > >> > >> 3. What happens if, instead of using your QTR* > instruments, you use > >> your predicted value (yhat) as your sole excluded > instrument in your > >> IV estimation with -ivreg2-, -ivregress- and -ivreg-? E.g., > >> > >> ivreg2 loghrearn age agesq YR* (educ=yhat), robust > >> > >> Cheers, > >> Mark > > * > * For searches and help try: > * http://www.stata.com/help.cgi?search > * http://www.stata.com/support/statalist/faq > * http://www.ats.ucla.edu/stat/stata/ > -- Heriot-Watt University is a Scottish charity registered under charity number SC000278. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/