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From |
"Henrique Neder" <hdneder@ufu.br> |

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

Subject |
st: using IV estimation with spatial econometrics |

Date |
Tue, 28 Feb 2012 14:52:46 -0300 |

Dear Stata list members I am making some estimates of spatial econometric models aiming to evaluate the impact of a particular credit program oriented to small farmers in Brazil. I have data in aggregated municipal level that are some economic and social indicators (the response variables, for example, difference in rural poverty rates, difference in Gini index, number of occupied people by farms), the indicator of credit (considered the primary causal variable in the models) and some control variables. There are concerns for endogeneity of the causal variable, both by reasons of reverse causality as for reasons of possible existence of correlation between this variable and unobservable variables. The strategy adopted is to reduce or eliminate the endogeneity bias by using cross section models of instrumental variables. One approach is the spivreg module usage, which in my view, focuses on the endogeneity of spatially lagged dependent variable and the endogeneity of other regressors in the right side of the equation. The second approach is the prior generation of the spatial lag of the dependent variable and later use of ivreg2 command. This command automatically perform several tests and save their results: 1) sub-identification and weak identification test, 2) a redundancy test of a excluded instruments sub-set, 3) over-identification restrictions test; 4) Exogeneity /orthogonality of suspected instruments test; 5) Test of one or more endogenous regressors on the estimation equation. In fact, I have more confidence in the estimate of causal variable parameter if all the results of these tests ensure the proper identification of the model. But only a few of several questions arise here: 1) some of the models (for some dependent variables) fail to prove the endogeneity of the regressors tested. This means that I abandon the IV-GMM estimation and stick with the first approach only? Unhappily, with this (spreg and spivreg command) I can?t perform tests. 2) In other cases, the test results seem to contradict each other: for example, in the sub-identification and weak identification test, the null is rejected and in the over-identifying restrictions testing the instruments are checked valid. This means that in this case I should get other more appropriate instruments? For the second approach some excluded instruments are the spatial lags of the control variables. What are the guarantees that these instruments are good and sufficient for my application? I think that they only treat the endogeneity of lagged space dependent variable. Like in spivreg command I need more instruments for implement with the ivreg2 command. Is there inappropriate to use this command in conjunction with spatial econometrics estimation with IV? Has anybody any good reference for this and other correlated questions? I would be grateful if someone made a comment about it. Thanks in advance Henrique Dantas Neder Professor at the Federal University of Uberlândia - Brazil Henrique Neder Prof. Associado - Instituto de Economia Universidade Federal de Uberlândia Tel.: (34) 32394157 Cel: (34) 91216600 * * 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/

**Follow-Ups**:**st: RE: using IV estimation with spatial econometrics***From:*"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk>

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