Bookmark and Share

Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

Re: st: RE: ivreg2 vs. Manual IV


From   Erkal Ersoy <[email protected]>
To   [email protected]
Subject   Re: st: RE: ivreg2 vs. Manual IV
Date   Mon, 7 Mar 2011 19:15:53 +0000

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.

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/


© Copyright 1996–2018 StataCorp LLC   |   Terms of use   |   Privacy   |   Contact us   |   Site index