Dear all,
How does one test for the different kinds of endogeneity and what is
the order of testing in STATA? The two possible kinds of endogeneity I
am dealing with are
(i) simultaneity or omitted variable endogeneity (I know I should use
the C test)
(ii) country-specific endogeneity (I know I should use the Hausman)
My dependent variable is growth. I have a cross-section with 31
countries and 6 four-year periods. With regards to (i) above, I have
10 exogenous included RHS variables and many exogenous excluded
instruments.
Also should I first for auto-correlation and heteroskedasticity in
STATA before testing for endogeneity or is is better to do the
reverse?
Thanks
On 3/26/09, Helene Ehrhart <[email protected]> wrote:
> Thank you Nicola for these helpful suggestions.
> I'll try them!
>
> Hélène.
>
> Quoting [email protected]:
>
>> No suggestions that 100% fit your problem.
>> Among second best solutions, you may leave the random effects. In
>> such a case, an uncommon choice is to try the -fmivreg- from
>> http://www.antonisureda.com/other/stuff/files/ which uses the Fama
>> and MacBeth (1973) two step procedure, with instrumental variables
>> and Newey-West standard errors. In the first step, for each single
>> time period a cross-sectional regression is performed. Then, in the
>> second step, the final coefficient estimates are obtained as the
>> average of the first step coefficient estimates.
>> Fama, Eugene F., and James D. MacBeth, 1973. Risk, Return, and
>> Equilibrium: Empirical tests, Journal of Political Economy 81,
>> 607-636.
>> Other options (not involving the fixed effects) are first
>> differencing (-xitvreg2- and -xtabond2-) or cross-sectional commands
>> (-ivreg2-, -newey2-), all of them are available from SSC.
>> Nicola
>>
>> P.S. I'll NOT receive/read any email but the Digest.
>>
>> At 02.33 21/03/2009 -0400, Helene Ehrhart wrote:
>>> Dear all,
>>>
>>> I would like to estimate an equation with instrumental variables using
>>> random effects and correcting for autocorrelation.
>>> I already tried many ways to do that in stata but no command was able
>>> to meet all the three requirements :
>>>
>>> - - xtivreg , re does not allow for autocorrelation correction
>>> - - xtivreg2 does not allow for random effects
>>> - - xtdata to transform data so that it corresponds to random effects
>>> and then estimation with ivreg28 but then the option bw(1) is not
>>> possible since the data are transformed
>>> - - estimating the first stage with xtreg ,re ; taking the predicted
>>> dependant value and using it as regressors for the 2nd stage using
>>> xtregar ,re which corrects for autocorrelation. This works but the
>>> standard errors should then be corrected by bootstrapping to correct
>>> the bias of using a predicted value as regressor. Unfortunately,
>>> traditional bootstrap using a random sample does not maintain the
>>> autocorrelation structure. So this method should also be eliminated.
>>>
>>> If anyone already faced this problem, estimating in 2SLS with random
>>> effects correcting for autocorrelation, I would really appreciate to
>>> know what is the proper way to do that in Stata.
>>>
>>> Thanks,
>>>
>>> Hélène Ehrhart.
>>
>>
>>
>
>
>
>
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