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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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