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 <[email protected]> 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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