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From |
"Schaffer, Mark E" <M.E.Schaffer@hw.ac.uk> |

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

Subject |
st: RE: using IV estimation with spatial econometrics |

Date |
Wed, 29 Feb 2012 13:23:53 -0000 |

Henrique, A short comment on just one of your points: > 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 isn't a contradiction. Rejection of the under- and weak-identification tests implies the model is identified (which I would guess is welcome news). Failure to reject the over-identifying restrictions implies these restrictions are satisfied (which is probably also welcome news). Cheers, Mark > -----Original Message----- > From: owner-statalist@hsphsun2.harvard.edu > [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of > Henrique Neder > Sent: 28 February 2012 17:53 > To: statalist@hsphsun2.harvard.edu > Subject: st: using IV estimation with spatial econometrics > > 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/ > -- Heriot-Watt University is a Scottish charity registered under charity number SC000278. Heriot-Watt University is the Sunday Times Scottish University of the Year 2011-2012 * * 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/

**References**:**st: using IV estimation with spatial econometrics***From:*"Henrique Neder" <hdneder@ufu.br>

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