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From |
Bond Tiger <bond0910@ymail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: IV-GMM |

Date |
Thu, 22 Jul 2010 09:38:41 -0700 (PDT) |

Dear all, I am trying to estimate a simultaneous equations model (involving 2 equations) and trying to estimate both the equations simultaneously (i.e. SYSTEM EQUATIONS ESTIMATION and NOT SINGLE EQUATION ESTIMATION). Following are my two equations: Y1=a0+a1*Y2+a2*L+e1...................(1) Y2=b0+b1*Y1+b2*K+e2..................(2) Variable Y1 is a continuous variable and Y2 is a dummy variable (with values 0 & 1), L & K are sets of other exogenous variables. Both L & K may have some common variables also. e1 & e2 are independent error terms. By model specification, I have assumed Y1 to be the endogenous in Eqn. (2) and Y2 to be endogenous in Eqn. (1). I have also tested for heteroskedasticity of disturbances and could detect presence of heteroskedasticity in my model. So I am trying to use Instrumental variable GMM estimation technique to estimate my model. I have considered different sets of instruments for Y1 & Y2. All my instruments are dummy variables (with 0 & 1 values). Now my questions are, (1) Since, all the instruments in my model are dummy variables (with values 1 & 0), can there be any efficiency loss due to use of dummy instrumental variables in estimation? Taking single equation setup to test my instruments, the instruments are all satisfying overidentifying restriction tests, however, they are appearing to be weak instruments in eqn. (1) with Y2 as endogenous regressor. For your information, I have tested weak instruments using Kleibergen-Paap (K-P) rk Wald F statistics and Stock-Yogo weak IV test critical values. (2) My K-P rk Wald F stat is < the Stock-Yogo critical values and therefore, the IVs are weak. Is this correct inference? (3) Do you think, since my instruments for the dummy regressor Y2 are also dummy variables (with values 0 & 1 and frequencies of 0s are more than 1s), therefore this weak instrument problem is arising? (4) How important is the Anderson-Rubin Wald test and Stock-Wright LM test statistics, to test significance of the endogenous regressors in the structural equation? In these tests, do rejection of H0 implies that the endogenous regressors are significant? Also do you have any idea about cluster robust estimates when there is within-cluster correlation? In my retirement savings analysis for workers in different firms, I have two data sets containing same variables. The only difference between the two data sets is that one data set (Say, data set 'A') contains duplicate Firm IDs (ID of different firms) and the other data set (data set 'B') contains no duplicate Firm IDs. Data set 'A' looks as following: (Firm IDs are 001, 002 & 003 and corresponding worker IDs are 001i, (i=1,....n) for each firm) FirmID WorkerID Wage 001 0011 $1000 001 0012 $2000 001 0013 $500 002 0021 $300 002 0022 $3000 002 0023 $500 003 0031 $600 003 0032 $1500 etc. and Data set 'B' looks as following (by deleting duplicate FirmID randomly): (Firm IDs are 001, 002 & 003 and corresponding worker IDs are 001i, (i=1,....n) for each firm) FirmID WorkerID Wage 001 0011 $1000 002 0023 $500 003 0032 $1500 etc. (5) With which data set should I use cluster() option? Any suggestions are highly welcome. I will greatly appreciate your kind help. Please, Please help me if possible. Regards, Bond * * 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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