Re: st: -cmp for mixed and nonrecursive process?

[Robert A Yaffee <bob.yaffee@nyu.edu>]

Jordana,
   I have a few preliminary questions?  Are these variables time-varying or not?   Do you have strong instruments for any of them? If so, how many?   Can you impose constraints?   Have you tested for identification of your model with an order or rank test?  If the parameters are time-varying, you will also have to be concerned with the stability of the feedback loops?   
   Assuming that you can impose enough constraints or have enough instruments, and that the resulting model is identified,  you might want to consider using a structured equation model approach with a polyserial-polychoric covariance matrix as inputs.  Check out Stas Kolenkov's work on confa for the application of such input.   How many control variables are you using? 
    Can you bifurcate the ordinal variable? 
             Robert
          
Robert A. Yaffee, Ph.D.
Research Professor
Silver School of Social Work
New York University

Biosketch: http://homepages.nyu.edu/~ray1/Biosketch2009.pdf

CV:  http://homepages.nyu.edu/~ray1/vita.pdf

----- Original Message -----
From: Jordana Rodrigues Cunha <jordana.rodrigues@unibo.it>
Date: Wednesday, November 24, 2010 10:16 am
Subject: st: -cmp for mixed and nonrecursive process?
To: "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>


> Dear statalisters, I really need your help, seems that this is an 
> impossible mission. I have consulted all the precedent faq's to arrive 
> until here, but without success.
> 
> I am estimating the effects of X, Z  and W over Y, where they are (in 
> sequence): a dummy, an ordinal, a dummy and a continuous variable. The 
> three independent variables are linked by a nonrecursive looping, 
> meaning that they are linked by reciprocal feedbacks: X determines Z 
> and W, as well as Z determines X and W and W determines Z and Y. 
> 
> X = a1+ bZ + cW + λ1Controls + e1; (binary dummy that varies from 0 to 
> 1)
> Z = a2 + dX + eW + λ2Controls + e2;(ordinal variable that varies form 
> 1 to 5)
> W = a3 + fX + gZ + λ3Controls + e3 ; (binary dummy that varies from 0 
> to 1)
> 
> the full model would be:
> 
> Y = a4 + hX + iZ + jW + λ4Controls + e4 (continuous variable)
> 
> 
> I have made simple probits and ordered probits to check the 
> relationship among the independent variables and I executed -cmp 
> models to check the correlations among their error terms (in pairs of 
> variables), confirming that they were really non-independent of each 
> other. The problem is that as -cmp doesn't allow for reciprocal 
> interaction among the variables and I included the second variable in 
> the first equation and omitted the first variable in the second 
> equation and so on. I have run, a naive OLS where the coefficients of 
> X and Z were significant but not the coefficient of  W. I think that 
> the best would run a SEM to use the 3 equations of X, Z and W with the 
> fourth Y in the full model structured to allow for endogeneity but I 
> have two main problems:
> 
> 1- I cannot execute a Hausmann test ( to check for endogeneity in the 
> full model) because when I ask for the residuals prediction after 
> running oprobit (for estimate the variable Z) this option is not 
> allowed and so I cannot regress the full model with the residual of Y 
> and check the significance of its coefficient.
> 
> 2- I have four different functional distributions and so if I would 
> like to do 2sls or 3sls in different stages I wouldn't know  how to 
> indicate the nature of the distribution for each variable and 3sls 
> assumes that all the variables are continuous, right?
> 
> 3 - I am using the same controls in all the equations, this could be a 
> problem?
> 
> 
> Please, somebody could give me an advice? I hope I had been clear in 
> explaining, thank you all in advance,
> 
> jordana
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