I am running on Stata 14.0 an ivregress 2sls estimation as in the following code.
Code:
. ivregress 2sls dmbel (rielmbel = gov soc env res dcpi lind dstocks i.code i.time), robust first
First-stage regressions
-----------------------
Number of obs = 2,747
F( 174, 2572) = 9.77
Prob > F = 0.0000
R-squared = 0.4741
Adj R-squared = 0.4385
Root MSE = 0.0850
------------------------------------------------------------------------------
| Robust
rielmbel | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
gov | -.0056843 .0007252 -7.84 0.000 -.0071063 -.0042622
soc | -.0017961 .0006894 -2.61 0.009 -.003148 -.0004443
env | .0083121 .0009661 8.60 0.000 .0064177 .0102065
res | -.0045355 .0010553 -4.30 0.000 -.0066048 -.0024663
dcpi | -.0275036 .0044332 -6.20 0.000 -.0361967 -.0188106
lind | -.15713 .0258762 -6.07 0.000 -.2078703 -.1063896
dstocks | .0008266 .0004389 1.88 0.060 -.000034 .0016872
|
code |
CHL | .0393731 .063514 0.62 0.535 -.0851706 .1639169
CHN | .9101593 .0773214 11.77 0.000 .7585408 1.061778
COL | -.301294 .0476558 -6.32 0.000 -.3947415 -.2078465
CZE | .1136015 .0721176 1.58 0.115 -.0278129 .255016
HUN | -.1895987 .0811023 -2.34 0.019 -.3486312 -.0305662
IDN | .0364302 .025311 1.44 0.150 -.0132017 .0860622
ISR | .0532919 .0848054 0.63 0.530 -.1130019 .2195858
KOR | .4274254 .0463959 9.21 0.000 .3364483 .5184025
MEX | .1617872 .034439 4.70 0.000 .0942562 .2293182
MYS | .1326777 .0602176 2.20 0.028 .0145979 .2507575
PER | -.3035272 .05881 -5.16 0.000 -.418847 -.1882074
PHL | -.2584554 .0476426 -5.42 0.000 -.3518772 -.1650336
POL | .2071764 .056128 3.69 0.000 .0971157 .3172371
ROU | -.2086394 .0610792 -3.42 0.001 -.3284088 -.0888699
RUS | .3532148 .0416844 8.47 0.000 .2714765 .4349532
THA | .1344817 .0526456 2.55 0.011 .0312496 .2377137
TUR | .0960361 .0388248 2.47 0.013 .0199051 .1721672
ZAF | .1614445 .0686339 2.35 0.019 .0268611 .2960278
|
time |
566 | .0081651 .0540451 0.15 0.880 -.0978112 .1141413
567 | -.0018779 .0533686 -0.04 0.972 -.1065276 .1027719
568 | .0073689 .054315 0.14 0.892 -.0991367 .1138745
569 | .0131488 .0550796 0.24 0.811 -.094856 .1211535
570 | .0035239 .0526657 0.07 0.947 -.0997476 .1067955
571 | -.0417565 .0453115 -0.92 0.357 -.1306073 .0470943
572 | -.0351463 .045288 -0.78 0.438 -.1239509 .0536583
573 | -.0181831 .0485004 -0.37 0.708 -.1132868 .0769207
574 | -.0039052 .0499676 -0.08 0.938 -.101886 .0940755
575 | -.0285183 .0465499 -0.61 0.540 -.1197973 .0627608
576 | -.0143531 .0488801 -0.29 0.769 -.1102014 .0814952
577 | -.068227 .0438727 -1.56 0.120 -.1542564 .0178024
578 | -.08955 .0430002 -2.08 0.037 -.1738686 -.0052315
579 | -.1002657 .0420257 -2.39 0.017 -.1826734 -.017858
580 | -.05097 .0442849 -1.15 0.250 -.1378076 .0358676
581 | -.0502664 .0438877 -1.15 0.252 -.1363253 .0357925
582 | -.0913523 .042259 -2.16 0.031 -.1742173 -.0084872
583 | -.08904 .0439348 -2.03 0.043 -.1751913 -.0028888
584 | -.0976524 .0438337 -2.23 0.026 -.1836054 -.0116994
585 | -.14857 .0462526 -3.21 0.001 -.239266 -.057874
586 | -.3533309 .0547859 -6.45 0.000 -.4607599 -.2459019
587 | -.3797358 .0544885 -6.97 0.000 -.4865817 -.27289
588 | -.3462651 .0517457 -6.69 0.000 -.4477327 -.2447976
589 | -.3553977 .0476362 -7.46 0.000 -.4488069 -.2619885
590 | -.4102855 .0509847 -8.05 0.000 -.5102608 -.3103102
591 | -.3337814 .0460526 -7.25 0.000 -.4240852 -.2434775
592 | -.2324673 .0415684 -5.59 0.000 -.3139783 -.1509563
593 | -.1843035 .0421199 -4.38 0.000 -.2668959 -.101711
594 | -.1558125 .0424002 -3.67 0.000 -.2389546 -.0726705
595 | -.1271502 .0433097 -2.94 0.003 -.2120757 -.0422247
596 | -.1174798 .0435129 -2.70 0.007 -.2028037 -.0321559
597 | -.1053721 .0447938 -2.35 0.019 -.1932077 -.0175365
598 | -.1093318 .0434922 -2.51 0.012 -.194615 -.0240485
599 | -.1337709 .0408405 -3.28 0.001 -.2138544 -.0536873
600 | -.1107745 .0410948 -2.70 0.007 -.1913568 -.0301922
601 | -.1310213 .0413355 -3.17 0.002 -.2120755 -.0499672
602 | -.1349439 .0401814 -3.36 0.001 -.213735 -.0561528
603 | -.1136856 .040691 -2.79 0.005 -.193476 -.0338952
604 | -.1125739 .0415963 -2.71 0.007 -.1941396 -.0310083
605 | -.1450647 .0436493 -3.32 0.001 -.230656 -.0594734
606 | -.1481128 .0429361 -3.45 0.001 -.2323056 -.0639199
607 | -.1345606 .0461171 -2.92 0.004 -.2249911 -.0441301
608 | -.1421602 .0444082 -3.20 0.001 -.2292397 -.0550807
609 | -.1279889 .0423831 -3.02 0.003 -.2110974 -.0448804
610 | -.110785 .0429728 -2.58 0.010 -.1950499 -.0265202
611 | -.133421 .0418201 -3.19 0.001 -.2154253 -.0514166
612 | -.1118164 .0430845 -2.60 0.010 -.1963003 -.0273325
613 | -.1248219 .0419105 -2.98 0.003 -.2070036 -.0426401
614 | -.1235164 .0405959 -3.04 0.002 -.2031203 -.0439125
615 | -.123387 .0399894 -3.09 0.002 -.2018018 -.0449723
616 | -.1215721 .0400539 -3.04 0.002 -.2001134 -.0430309
617 | -.1318429 .0397651 -3.32 0.001 -.2098178 -.053868
618 | -.1403435 .0395989 -3.54 0.000 -.2179925 -.0626945
619 | -.137319 .0408709 -3.36 0.001 -.2174621 -.0571758
620 | -.1846433 .0424692 -4.35 0.000 -.2679206 -.1013659
621 | -.2501755 .0436816 -5.73 0.000 -.3358301 -.1645209
622 | -.2015664 .0417181 -4.83 0.000 -.2833709 -.1197619
623 | -.2395515 .0440191 -5.44 0.000 -.325868 -.153235
624 | -.2306571 .0431127 -5.35 0.000 -.3151963 -.1461179
625 | -.2230069 .0424552 -5.25 0.000 -.3062568 -.1397571
626 | -.1901461 .040798 -4.66 0.000 -.2701464 -.1101459
627 | -.1763518 .0416542 -4.23 0.000 -.258031 -.0946727
628 | -.1941324 .0431574 -4.50 0.000 -.2787592 -.1095055
629 | -.2383907 .0453307 -5.26 0.000 -.3272791 -.1495023
630 | -.205805 .0418966 -4.91 0.000 -.2879595 -.1236504
631 | -.1746416 .0412912 -4.23 0.000 -.255609 -.0936742
632 | -.1599838 .0409211 -3.91 0.000 -.2402255 -.0797421
633 | -.1301993 .0405216 -3.21 0.001 -.2096576 -.050741
634 | -.1302085 .0404654 -3.22 0.001 -.2095565 -.0508605
635 | -.1220317 .0404359 -3.02 0.003 -.201322 -.0427414
636 | -.1054495 .0400379 -2.63 0.008 -.1839592 -.0269398
637 | -.1181236 .0398405 -2.96 0.003 -.1962463 -.0400008
638 | -.1194951 .0402626 -2.97 0.003 -.1984455 -.0405447
639 | -.1323093 .0406713 -3.25 0.001 -.2120611 -.0525575
640 | -.1152404 .0400866 -2.87 0.004 -.1938457 -.0366351
641 | -.1202925 .0407074 -2.96 0.003 -.2001151 -.0404699
642 | -.162043 .0411493 -3.94 0.000 -.2427322 -.0813539
643 | -.1661865 .0417703 -3.98 0.000 -.2480934 -.0842797
644 | -.1807564 .0433515 -4.17 0.000 -.2657639 -.095749
645 | -.1551145 .041657 -3.72 0.000 -.2367992 -.0734298
646 | -.142005 .0419176 -3.39 0.001 -.2242007 -.0598093
647 | -.1529248 .0430962 -3.55 0.000 -.2374315 -.068418
648 | -.1507832 .0433217 -3.48 0.001 -.2357321 -.0658343
649 | -.1651797 .04394 -3.76 0.000 -.2513412 -.0790183
650 | -.1390598 .0411616 -3.38 0.001 -.2197729 -.0583466
651 | -.14292 .040973 -3.49 0.000 -.2232634 -.0625765
652 | -.1309518 .0410339 -3.19 0.001 -.2114146 -.0504889
653 | -.1148193 .0405231 -2.83 0.005 -.1942806 -.0353581
654 | -.1123831 .0408201 -2.75 0.006 -.1924268 -.0323395
655 | -.1212662 .0409699 -2.96 0.003 -.2016036 -.0409287
656 | -.1097958 .040625 -2.70 0.007 -.1894569 -.0301347
657 | -.1195196 .0413159 -2.89 0.004 -.2005353 -.0385038
658 | -.1135709 .0407567 -2.79 0.005 -.1934902 -.0336516
659 | -.1042169 .0409735 -2.54 0.011 -.1845613 -.0238726
660 | -.1255047 .0416624 -3.01 0.003 -.2072 -.0438095
661 | -.1425736 .0431596 -3.30 0.001 -.2272046 -.0579425
662 | -.117195 .0425727 -2.75 0.006 -.2006751 -.0337148
663 | -.1348688 .0435859 -3.09 0.002 -.2203358 -.0494018
664 | -.1280062 .0429813 -2.98 0.003 -.2122876 -.0437247
665 | -.1289821 .0431838 -2.99 0.003 -.2136607 -.0443035
666 | -.1323969 .04368 -3.03 0.002 -.2180484 -.0467454
667 | -.143292 .0440901 -3.25 0.001 -.2297477 -.0568363
668 | -.1713126 .0463701 -3.69 0.000 -.2622391 -.0803861
669 | -.2094114 .0526716 -3.98 0.000 -.3126944 -.1061283
670 | -.1659607 .047872 -3.47 0.001 -.2598324 -.072089
671 | -.1628071 .0478387 -3.40 0.001 -.2566134 -.0690008
672 | -.1601802 .0459658 -3.48 0.001 -.2503139 -.0700466
673 | -.1784725 .0474774 -3.76 0.000 -.2715702 -.0853747
674 | -.188288 .0470512 -4.00 0.000 -.28055 -.0960261
675 | -.1389032 .0445062 -3.12 0.002 -.2261747 -.0516316
676 | -.1319502 .0442777 -2.98 0.003 -.2187738 -.0451266
677 | -.1320015 .0436834 -3.02 0.003 -.2176596 -.0463434
678 | -.1142698 .0429019 -2.66 0.008 -.1983956 -.030144
679 | -.1127285 .0422778 -2.67 0.008 -.1956304 -.0298266
680 | -.0896584 .0420153 -2.13 0.033 -.1720456 -.0072712
681 | -.09995 .0430073 -2.32 0.020 -.1842824 -.0156177
682 | -.1025734 .0427267 -2.40 0.016 -.1863557 -.0187911
683 | -.1245426 .0423403 -2.94 0.003 -.207567 -.0415181
684 | -.1034273 .0416639 -2.48 0.013 -.1851255 -.021729
685 | -.0970144 .042589 -2.28 0.023 -.1805266 -.0135022
686 | -.0844257 .0421653 -2.00 0.045 -.167107 -.0017444
687 | -.075377 .0430377 -1.75 0.080 -.1597691 .0090152
688 | -.071149 .0430912 -1.65 0.099 -.155646 .013348
689 | -.0723727 .0428111 -1.69 0.091 -.1563205 .0115751
690 | -.0663081 .0416831 -1.59 0.112 -.1480439 .0154277
691 | -.0541647 .0410407 -1.32 0.187 -.1346408 .0263114
692 | -.0431412 .0416508 -1.04 0.300 -.1248138 .0385313
693 | -.0461507 .0423142 -1.09 0.276 -.1291241 .0368226
694 | -.0477742 .0430899 -1.11 0.268 -.1322686 .0367202
695 | -.0433186 .0439681 -0.99 0.325 -.1295351 .0428979
696 | -.0432889 .042812 -1.01 0.312 -.1272384 .0406607
697 | -.0307226 .0435008 -0.71 0.480 -.1160229 .0545776
698 | -.0407607 .0434061 -0.94 0.348 -.1258752 .0443538
699 | -.0391918 .0442075 -0.89 0.375 -.1258778 .0474942
700 | -.041366 .0440631 -0.94 0.348 -.1277688 .0450369
701 | -.0579426 .0439573 -1.32 0.188 -.1441379 .0282526
702 | -.0648714 .0447735 -1.45 0.147 -.1526671 .0229243
703 | -.0469431 .0443466 -1.06 0.290 -.1339017 .0400156
704 | -.078142 .0429083 -1.82 0.069 -.1622804 .0059963
705 | -.060707 .0430631 -1.41 0.159 -.1451488 .0237349
706 | -.0827343 .0445174 -1.86 0.063 -.1700279 .0045593
707 | -.0747965 .044787 -1.67 0.095 -.1626186 .0130256
708 | -.0777714 .0442149 -1.76 0.079 -.1644718 .008929
709 | -.0472163 .0449729 -1.05 0.294 -.135403 .0409704
710 | -.0371848 .0455833 -0.82 0.415 -.1265684 .0521988
711 | -.0577427 .0431851 -1.34 0.181 -.1424238 .0269383
712 | -.0428744 .0435946 -0.98 0.325 -.1283585 .0426097
713 | -.0706304 .0436904 -1.62 0.106 -.1563024 .0150415
714 | -.0501905 .0446396 -1.12 0.261 -.1377236 .0373426
|
_cons | 3.718266 .6176449 6.02 0.000 2.507135 4.929398
------------------------------------------------------------------------------
Instrumental variables (2SLS) regression Number of obs = 2,747
Wald chi2(1) = 18.52
Prob > chi2 = 0.0000
R-squared = 0.0317
Root MSE = .04316
------------------------------------------------------------------------------
| Robust
dmbel | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
rielmbel | .0618787 .0143775 4.30 0.000 .0336994 .090058
_cons | .0007128 .0008038 0.89 0.375 -.0008626 .0022882
------------------------------------------------------------------------------
Instrumented: rielmbel
Instruments: gov soc env res dcpi lind dstocks 2.code 3.code 4.code 5.code
6.code 7.code 8.code 9.code 10.code 11.code 12.code 13.code
14.code 15.code 16.code 17.code 18.code 19.code 566.time
567.time 568.time 569.time 570.time 571.time 572.time
573.time 574.time 575.time 576.time 577.time 578.time
579.time 580.time 581.time 582.time 583.time 584.time
585.time 586.time 587.time 588.time 589.time 590.time
591.time 592.time 593.time 594.time 595.time 596.time
597.time 598.time 599.time 600.time 601.time 602.time
603.time 604.time 605.time 606.time 607.time 608.time
609.time 610.time 611.time 612.time 613.time 614.time
615.time 616.time 617.time 618.time 619.time 620.time
621.time 622.time 623.time 624.time 625.time 626.time
627.time 628.time 629.time 630.time 631.time 632.time
633.time 634.time 635.time 636.time 637.time 638.time
639.time 640.time 641.time 642.time 643.time 644.time
645.time 646.time 647.time 648.time 649.time 650.time
651.time 652.time 653.time 654.time 655.time 656.time
657.time 658.time 659.time 660.time 661.time 662.time
663.time 664.time 665.time 666.time 667.time 668.time
669.time 670.time 671.time 672.time 673.time 674.time
675.time 676.time 677.time 678.time 679.time 680.time
681.time 682.time 683.time 684.time 685.time 686.time
687.time 688.time 689.time 690.time 691.time 692.time
693.time 694.time 695.time 696.time 697.time 698.time
699.time 700.time 701.time 702.time 703.time 704.time
705.time 706.time 707.time 708.time 709.time 710.time
711.time 712.time 713.time 714.time
. estat overid
Test of overidentifying restrictions:
Score chi2(173) = 788.538 (p = 0.0000)
. estat endogenous
Tests of endogeneity
Ho: variables are exogenous
Robust score chi2(1) = .831163 (p = 0.3619)
Robust regression F(1,2744) = .831294 (p = 0.3620)
.
end of do-file
.I would like to ask you three questions, please.
- Is including time dummies in the first step a legitimate choice? If omitted, the second step regression would lose significance. However, I am not sure about the theoretical soundness of this approach.
- The estat overid test rejects the null hypothesis of exogenous instruments. Given the large number of instruments, I suspect many of them are actually endogenous, but some may not. So how can I test for exogeneity on a subset of instruments?
- The estat endogenous test fails to reject the null hypothesis of exogenous independent variable, so that OLS would be more efficient than 2SLS. However, I know that the endogeneity test is only meaningful in the presence of valid instruments. Since the test of overidentifying restrictions rejects the null of exogenous instruments, I would conclude I am not allowed to opt for OLS. Please can you confirm my interpretation is correct? (Below the output of the OLS regression for comparison purposes.)
Code:
. reg dmbel rielmbel, robust
Linear regression Number of obs = 2,968
F(1, 2966) = 30.56
Prob > F = 0.0000
R-squared = 0.0349
Root MSE = .04341
------------------------------------------------------------------------------
| Robust
dmbel | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
rielmbel | .0728101 .0131701 5.53 0.000 .0469865 .0986336
_cons | .0010476 .000795 1.32 0.188 -.0005113 .0026065
------------------------------------------------------------------------------
.
end of do-file
.Best,
Daniele Veggiato
0 Response to Time dummies in IV regression and interpretation of post-estimation
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