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Re: st: interpretation reciprocal causation ivprobit cdsimeq


From   Nick Cox <njcoxstata@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: interpretation reciprocal causation ivprobit cdsimeq
Date   Tue, 30 Oct 2012 09:56:46 +0000

You don't give full references, as requested in the Statalist FAQ.

That detail of etiquette, however, doesn't explain why you got no
answer. In this case, they look like standard references that anyone
acquainted with your field would recognise (nevertheless, you are
still asked to give full references).

As I posted yesterday, there are about 5000 members of Statalist, and
simply but importantly I can't speak for anyone else. What follows is
a personal guess. Most of those 5000 people don't post anything, and
that's great, because otherwise the list would collapse. It's like my
relationship with my newspaper: I read what looks interesting or
useful to me, ignore most of it, and feel no obligation to write to
it.

First off, this is an intensely econometric question. That cuts down
the number of people interested and competent to say anything at all,
and cuts me out, for example.

Questions broadly like yours are quite common on Statalist. They are
certainly allowed. But in practice they are often unanswered.

My impression is that you do a very good job of explaining what you
are trying, but the root of it is that you want advice on correctness
of conclusions and interpretation of results. In essence, that's a
pretty tough call for anyone; even people working on similar or
identical problems would have difficulty giving an answer that is
concise, precise and helpful.

It's difficult to know whether a question will be answered. Sometimes
a poster hits the jackpot: someone on the list knows the same problem
and say something useful. Sometimes not.

A study of the archives -- look at thread indexes such as
<http://www.stata.com/statalist/archive/2012-10/index.html> -- will
show many good questions that went unanswered.

In short, I don't think there is an obvious way of making your
question better. It's just a difficult question to answer and no-one
so far has felt moved to respond.

Beyond the FAQ there's generic advice at

<http://www.stata.com/statalist/archive/2012-10/msg00174.html>

<http://blog.stata.com/2010/12/14/how-to-successfully-ask-a-question-on-statalist/>

Nick

On Tue, Oct 30, 2012 at 8:26 AM,  <Marianne.LEFEBVRE@ec.europa.eu> wrote:

> As I am new here, I would like to understand how to improve my question sent ten days ago in order to get your feedback. It is about the interpretation of the results of two estimations procedures ivprobit. Do not hesitate to let me know if this is not the good place to ask such questions or good question format.

Marianne.LEFEBVRE@ec.europa.eu

> I have run the following regressions using ivprobit and cdsimeq and I am not too sure about the interpretation. please see my question in capital letters below. Thanks a lot for your help.
>
>
>  In order to account for the potential endogeneity between insurance decision (binary variable) and economic performance (continuous), we adopt a 2SLS estimation technique where total gross margin is instrumented. We use Newey's (1987) minimum-chi-squared estimator (ivprobit twostep option). We find that economic performance, as defined by the total gross margin, significantly explains insurance adoption (table 1).  Post-estimation tests: We ran the joint significance test of the instruments in the first stage regression (F-statistic>10). The Amemiya-Lee-Newey test of overidentifying restrictions is not significant (chi2=2.025, p-value= 0.1547). The Wald test of exogeneity for IVprobit estimations allows to reject the null hypothesis of exogeneity of the instruments (chi2=7.45, p-value= 0.0064).
>
> Then, we verify whether there is reciprocal causation between insurance use and economic performance (total gross margin). To obtain this result, we rely on the two-stage probit least squares estimation method described in (Maddala 1983) for simultaneous equations models in which one of the endogenous variables is continuous (total gross margin) and the other endogenous variable is dichotomous (insurance use) (cdsimeq command in Stata http://www.stata-journal.com/article.html?article=st0038). We find that economic performance (total gross margin) significantly explains insurance adoption but the reverse effect is not significant (table 2).
>
> IS IT CORRECT TO CONCLUDE AS FOLLOWS?
> The result suggests that the endogeneity bias between insurance decision and economic performance is due to omitted variables, and not reciprocal causation. It therefore justifies the use of the ivprobit model where economic performance is instrumented to explain insurance decision, rather than the (Maddala 1983)  estimation procedure (cdsimeq).
>
> Table 1: 2SLS Probability to adopt insurance, with instrumentation of gross margin
>
> First step
> Number of obs =     144
> R-squared     =  0.2453
> Adj R-squared =  0.1946
>
> grossmargin                             Coef.   Std. Err.      t    P>t     [95% Conf. Interval]
>
> q3_individual farms             -228188.2***   112400.5    -2.03   0.044    -450496.7   -5879.677
> q4_totaluaa_sq                      .1313854 ***  .0329142     3.99   0.000     .0662869    .1964839
> nuts2_32                                51374.23   95259.15     0.54   0.591    -137031.8    239780.2
> nuts2_33                                16731.56   100579.9     0.17   0.868      -182198    215661.2
> nuts2_34                                10620.46   100359.5     0.11   0.916    -187873.1      209114
> nuts2_41                                 7942.025   127155.7     0.06   0.950    -243549.8    259433.8
> nuts2_42                                  93651.27    99561.3     0.94   0.349    -103263.6    290566.2
> q4_ratiorent                           -33656.87   82139.63    -0.41   0.683    -196114.8      128801
> q21_noninsuranmeasures       -56509.8   68454.26    -0.83   0.411    -191900.4     78880.8
> _cons                                       292010.1   154708.1     1.89   0.061    -13975.68    597995.8
>
> Second step
> Number of obs   =       144
> Wald chi2(8)    =     29.93
> Prob > chi2     =    0.0002
>
> insurance2011                                             Coef.   Std. Err.      z    P>z     [95% Conf. Interval]
>
> I_grossmargin                       3.88e-06***   1.43e-06     2.72   0.006     1.09e-06    6.68e-06
> nuts2_32                                -1.805997   .5538496    -3.26   0.001    -2.891522   -.7204715
> nuts2_33                                   -1.224679   .5420211    -2.26   0.024    -2.287021   -.1623367
> nuts2_34                                   -.9044687   .5287984    -1.71   0.087    -1.940894     .131957
> nuts2_41                                  -2.162879   .7412688    -2.92   0.004    -3.615739   -.7100187
> nuts2_42                                   -3.11869   .7796749    -4.00   0.000    -4.646824   -1.590555
> q4_ratiorent                               1.127435   .4558475     2.47   0.013     .2339906     2.02088
> q21_noninsuranmeasures          -.7945278   .3990442    -1.99   0.046     -1.57664   -.0124156
> _cons                                          .4889867   .4838786     1.01   0.312     -.459398    1.437371
>
> Wald test of exogeneity:     chi2(1) =     7.45           Prob > chi2 = 0.0064
> Test of overidentifying restrictions: Amemiya-Lee-Newey minimum chi-sq statistic     Chi-sq(1)= 2.025     P-value = 0.1547
>
>
>
> Table 2: two-stage probit least squares estimation (cdsimeq) –
> SECOND STAGE REGRESSIONS WITH CORRECTED STANDARD ERRORS
>
>
> ------------------------------------------------------------------------------
>  grossmargin |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> I_insur~2011 |  -15809.74   28736.81    -0.55   0.583    -72623.96    41004.48
> q3_individ~s |  -239284.4   113697.5    -2.10   0.037    -464070.5   -14498.42
> q4_totalua~q |   .1351443   .0322533     4.19   0.000     .0713778    .1989108
>        _cons |   264916.4   104230.9     2.54   0.012     58846.34    470986.5
> ------------------------------------------------------------------------------
> ------------------------------------------------------------------------------
> insuran~2011 |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> I_grossmar~n |   2.67e-06***   1.07e-06     2.50   0.012     5.77e-07    4.77e-06
>     nuts2_32 |  -1.634341   .5218917    -3.13   0.002     -2.65723   -.6114523
>     nuts2_33 |  -1.201044   .5233067    -2.30   0.022    -2.226706   -.1753817
>     nuts2_34 |  -.8801489   .5140605    -1.71   0.087    -1.887689    .1273911
>     nuts2_41 |  -2.143736   .7280761    -2.94   0.003    -3.570739   -.7167331
>     nuts2_42 |  -2.832533   .6944463    -4.08   0.000    -4.193622   -1.471443
> q4_ratiorent |    1.11448    .445112     2.50   0.012     .2420765    1.986884
> q21_nonins~s |   -.755226    .381161    -1.98   0.048    -1.502288   -.0081642
>        _cons |    .483358   .4681395     1.03   0.302    -.4341785    1.400894
> ------------------------------------------------------------------------------

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