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st: RE: using IV estimation with spatial econometrics


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  
> 
> 
> 
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> *   http://www.ats.ucla.edu/stat/stata/
> 


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