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From |
"Dithmer, Jan" <jdithme@food-econ.uni-kiel.de> |

To |
"statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu> |

Subject |
st: AW: RE: AW: Use of xtabond2 |

Date |
Mon, 17 Jun 2013 09:32:39 +0000 |

Mavilde, it's all explained in the help file of the command xtabond2 (just type "help xtabond2" in the command line) Look at the examples presented at the end of the help file: There you'll find "Three ways to reduce the instrument count"! In your case, just add "collapse" behind lag(2 2)), thus gmm(logGVApc, lag(2 2) collapse) Best, Jan -----Ursprüngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Mavilde Modesto Gesendet: Sunday, June 16, 2013 11:15 PM An: statalist@hsphsun2.harvard.edu Betreff: st: RE: AW: Use of xtabond2 Thank you of your answer! I already worked those papers! I think I should solve the problem of "too much instruments" with such a command "collapse" that I do not know how to incorporate in the following one: xi: xtabond2 logGVApc l.logGVApc logPubInvpc logProductiv i.yearid,gmm(logGVApc,lag(2 2)) iv(i.yearid logPubInvpc) robust twostep small I didn't find an example in those papers! Would you be so kind helping me? Thanks a lot! Mavilde Modesto -----Mensagem original----- De: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Em nome de Dithmer, Jan Enviada: segunda-feira, 10 de Junho de 2013 8:45 Para: statalist@hsphsun2.harvard.edu Assunto: st: AW: Use of xtabond2 Dear Mavilde, you should first work through the paper from the author of the routine xtabond2, which describes the program in great detail: David Roodman, 2009. "How to do xtabond2: An introduction to difference and system GMM in Stata," Stata Journal, StataCorp LP, vol. 9(1), pages 86-136, March. The generalized inverse to calculate the optimal weighting matrix is generated automatically. The warning "Number of instruments may be large relative to number of observations" points to the fact that your number of instruments is higher than your number of groups as a rule of thumb to indicate that there may be problems of instrument proliferation. To understand the potential problems coming with a large instrument set, read: David Roodman, 2009. "A Note on the Theme of Too Many Instruments," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(1), pages 135-158, 02. Best, Jan -----Ursprüngliche Nachricht----- Von: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] Im Auftrag von Mavilde Modesto Gesendet: Sunday, June 09, 2013 12:36 AM An: 'Sergiy Radyakin' Cc: owner-statalist@hsphsun2.harvard.edu Betreff: st: Use of xtabond2 Dear Statalist, I am a new user of Stata and I do not Know enough to solve those Two "Warnings" I have got running xtabond2. The first one I do not understand because I use 49 instruments but I have got 510 observations. About the second warning anyone can help me indicating which command should I use to produce and use the generalized inverse to calculate optimal weighting matrix for two-step estimation and how to proceed? I would be so grateful! Here are the results I have got: xi: xtabond2 logGVApc l.logGVApc logPubInvpc logProductiv i.yearid,gmm(logGVApc,lag(2 2)) iv(i.yearid) robust twostep small/*2 lag*//*considering Public Investment as exogenous*/ i.yearid _Iyearid_1-18 (naturally coded; _Iyearid_1 omitted) Favoring speed over space. To switch, type or click on mata: mata set matafavor space, perm. _Iyearid_18 dropped due to collinearity Warning: Number of instruments may be large relative to number of observations. Warning: Two-step estimated covariance matrix of moments is singular. Using a generalized inverse to calculate optimal weighting matrix for two-step estimation. Difference-in-Sargan/Hansen statistics may be negative. Dynamic panel-data estimation, two-step system GMM Group variable: id Number of obs = 510 Time variable : Periodo Number of groups = 30 Number of instruments = 49 Obs per group: min = 17 avg = 17.00 F(19, 29) = 967.14 Prob > F = 0.000 max = 17 logGVApc Coef. Corrected Std. Err. t P>t [95% Conf. Interval] logGVApc L1. .682456 .1063249 6.42 0.000 .4649971 .8999149 logPubInvpc -.0131582 .033408 -0.39 0.697 -.0814852 .0551688 logProductiv .3087351 .1130752 2.73 0.011 .0774704 .5399997 _Iyearid_2 -.0068182 .0563709 -0.12 0.905 -.1221097 .1084733 _Iyearid_3 .0011161 .0454337 0.02 0.981 -.0918064 .0940385 _Iyearid_4 -.0114692 .0372671 -0.31 0.760 -.0876889 .0647506 _Iyearid_5 .0045814 .0361524 0.13 0.900 -.0693586 .0785214 _Iyearid_6 -.0350082 .03002 -1.17 0.253 -.096406 .0263896 _Iyearid_7 -.0075198 .0267703 -0.28 0.781 -.0622712 .0472316 _Iyearid_8 -.0195811 .028722 -0.68 0.501 -.0783242 .039162 _Iyearid_9 .0031761 .020015 0.16 0.875 -.0377591 .0441114 _Iyearid_10 .0003316 .0215934 0.02 0.988 -.0438319 .044495 _Iyearid_11 .0088895 .0172075 0.52 0.609 -.0263038 .0440827 _Iyearid_12 .0194218 .0134674 1.44 0.160 -.0081222 .0469658 _Iyearid_13 .0252198 .0125812 2.00 0.054 -.0005116 .0509512 _Iyearid_14 .0273918 .0257642 1.06 0.296 -.0253019 .0800856 _Iyearid_15 .011291 .0230309 0.49 0.628 -.0358124 .0583944 _Iyearid_16 .0043055 .0191247 0.23 0.823 -.0348089 .0434199 _Iyearid_17 .0141141 .0165234 0.85 0.400 -.0196801 .0479083 _cons -.2379976 .3225759 -0.74 0.467 -.8977395 .4217443 Instruments for first differences equation Standard D.(_Iyearid_2 _Iyearid_3 _Iyearid_4 _Iyearid_5 _Iyearid_6 _Iyearid_7 _Iyearid_8 _Iyearid_9 _Iyearid_10 _Iyearid_11 _Iyearid_12 _Iyearid_13 _Iyearid_14 _Iyearid_15 _Iyearid_16 _Iyearid_17 _Iyearid_18) GMM-type (missing=0, separate instruments for each period unless collapsed) L2.logGVApc Instruments for levels equation Standard _Iyearid_2 _Iyearid_3 _Iyearid_4 _Iyearid_5 _Iyearid_6 _Iyearid_7 _Iyearid_8 _Iyearid_9 _Iyearid_10 _Iyearid_11 _Iyearid_12 _Iyearid_13 _Iyearid_14 _Iyearid_15 _Iyearid_16 _Iyearid_17 _Iyearid_18 _cons GMM-type (missing=0, separate instruments for each period unless collapsed) DL.logGVApc Arellano-Bond test for AR(1) in first differences: z = -2.60 Pr > z = 0.009 Arellano-Bond test for AR(2) in first differences: z = 1.33 Pr > z = 0.185 Sargan test of overid. restrictions: chi2(29) = 68.55 Prob > chi2 = 0.000 (Not robust, but not weakened by many instruments.) Hansen test of overid. restrictions: chi2(29) = 17.64 Prob > chi2 = 0.951 (Robust, but weakened by many instruments.) Difference-in-Hansen tests of exogeneity of instrument subsets: GMM instruments for levels Hansen test excluding group: chi2(13) = 17.64 Prob > chi2 = 0.172 Difference (null H = exogenous): chi2(16) = 0.00 Prob > chi2 = 1.000 iv(_Iyearid_2 _Iyearid_3 _Iyearid_4 _Iyearid_5 _Iyearid_6 _Iyearid_7 _Iyearid_8 _Iyearid_9 _Iyearid_10 _Iyearid_11 _Iyearid_12 _Iyearid_13 _Iyearid_14 _Iyearid_15 _Iyearid_16 _Iyearid_17 _Iyearid_18) Hansen test excluding group: chi2(13) = 10.44 Prob > chi2 = 0.657 Difference (null H = exogenous): chi2(16) = 7.20 Prob > chi2 = 0.969 Mavilde Modesto Universidade Católica Portuguesa * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Use of xtabond2***From:*"Mavilde Modesto" <mavildemodesto@netcabo.pt>

**st: AW: Use of xtabond2***From:*"Dithmer, Jan" <jdithme@food-econ.uni-kiel.de>

**st: RE: AW: Use of xtabond2***From:*"Mavilde Modesto" <mavildemodesto@netcabo.pt>

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