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RE: st: RE: ivreg2 vs. Manual IV
From
"Schaffer, Mark E" <[email protected]>
To
<[email protected]>
Subject
RE: st: RE: ivreg2 vs. Manual IV
Date
Mon, 7 Mar 2011 20:05:08 -0000
Erkal,
> -----Original Message-----
> From: [email protected]
> [mailto:[email protected]] On Behalf Of Erkal Ersoy
> Sent: 07 March 2011 19:16
> To: [email protected]
> 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
> <[email protected]> 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/
>
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registered under charity number SC000278.
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