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Re: st: Iv reg estimates are too large in stnd errors


From   sjsamuels@gmail.com
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Iv reg estimates are too large in stnd errors
Date   Fri, 20 Nov 2009 10:35:31 -0500

I think that Shruti is trying to emulate the analysis in Angrist and
Evans, 1998, which saw much larger effects of  morethan2children.

-Steve

Reference:
 #Children and Their Parents' Labor Supply: Evidence from Exogenous
Variation in Family Size
# Joshua D. Angrist and William N. Evans
# The American Economic Review, Vol. 88, No. 3 (Jun., 1998), pp. 450-477

On Fri, Nov 20, 2009 at 10:08 AM, Austin Nichols
<austinnichols@gmail.com> wrote:
> I second Maarten: the large SE reflects the large variance inherent in
> IV.  Note that http://papers.nber.org/papers/w10281 indicates the
> effect of sex mix on subsequent fertility is about .02 to .04 so you
> will not be using a lot of the variation in your endog var.
>
> However: note two other points--if you have survey data, you should
> not use [aw= but instead [pw= and you should cluster to get more
> correct SEs.
> Also, you have a binary RHS endog var and binary outcome so you may
> prefer another estimator, e.g. -biprobit- or -cmp- (on SSC).
>
> Also, why not consider boyfirst an excluded instrument?  Is the worry
> that some families who observe the sex before birth choose not to have
> a girl first?
>
> On Fri, Nov 20, 2009 at 8:13 AM, Maarten buis <maartenbuis@yahoo.co.uk> wrote:
>> --- On Fri, 20/11/09, Shruti Kapoor wrote:
>>> I am using ivreg for the first time and am not sure if i
>>> can do anything to improve my results. The biggest problem
>>> i am facing is that the stnd errors on my endogenous variable
>>> (morethan2children, even when instrumented) is quite high.
>>> Which makes them insignificant.
>>
>> In general, large standard errors are not a problem, they are
>> a finding. We may or may not like that finding, but that is
>> irrelevant.
>>
>> Specifically with instrumental variables, I am not surprised
>> that you find large standard errors. Instrumental variables can
>> potentially provide you with a very strong argument that the
>> effect you found is likely to be causal, but there is always a
>> price to be paid: in the case of instrumental variable the
>> price is low power (i.e. large standard errors). As the
>> economists say: there is no such thing as a free lunch.
>>
>> -- Maarten
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-- 
Steven Samuels
sjsamuels@gmail.com
18 Cantine's Island
Saugerties NY 12477
USA
845-246-0774

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