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Re: st: test of significant between coefficients

From   Nick Cox <[email protected]>
To   [email protected]
Subject   Re: st: test of significant between coefficients
Date   Tue, 27 Sep 2011 17:16:40 +0100

See also the thread starting here


On Tue, Sep 27, 2011 at 4:35 PM, Nick Cox <[email protected]> wrote:
> Richard's answer overlaps with mine, which is fine.
> I want to underline the idea that often coefficients should be thought
> as being bundled together. For example, if a cosine term is included
> in a model a sine term should be too. Leaving out one or the other can
> omit some useful information about phase even if one coefficient is
> not significant. A more widely familiar example is a set of
> indicators. Degrading them so that all are significant just coarsens a
> model.
> Come to think of it, we've have had this discussion before. Just
> search for "Richard Williams" in the Statalist archives.
> Nick
> On Tue, Sep 27, 2011 at 3:48 PM, Richard Williams
> <[email protected]> wrote:
>> At 10:35 AM 9/27/2011, Andrea Rispoli wrote:
>>> Dear Statalisters,
>>> I am running a test of significance between two coefficients of the
>>> same OLS regression.
>>> My question is : if the two coefficients are not significant, does it
>>> still make sense to conduct the test? I am asking because sometimes
>>> while the individual coefficients are not significant the difference
>>> between them is significant, so I was trying to understand the meaning
>>> of this result.
>>> Thank you!
>>> AR
>> It can happen. The individual tests are testing whether the coefficients
>> equal zero. The equality test might be testing whether, say, -.5
>> significantly differs from .5. In any event, there is nothing that says all
>> your tests have to be logically consistent with each other. The overall F or
>> chi-square statistic might be significant for a model, while none of the
>> individual coefficients are.
>> A more common situation might be where a coefficient is significant in one
>> group but not in another. I always warn my students to be careful about
>> saying X is important for one group but not the other. If, say, you are
>> comparing whites and black, your white sample size might be much larger,
>> which can help the effect to achieve significance for whites but not blacks.
>> The actual estimated coefficients, however, may be quite similar.
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