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Re: st: A layman question on model building


From   James Bernard <jamesstatalist@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: A layman question on model building
Date   Thu, 7 Mar 2013 15:53:20 +0800

Thanks John,

Very helpful!




On Thu, Mar 7, 2013 at 3:22 PM, John Antonakis <John.Antonakis@unil.ch> wrote:
> Hi:
>
> If you have a sufficiently large sample size and the regressors of interest
> are significant predictors, then it is best to leave in the controls; they
> do not harm but help consistency (even if only a tad). What suffers is
> efficiency (standard errors) and this is amplified in small sample size
> conditions. I would (mostly) always err on the side of caution and include
> the controls. A couple of things to do before considering dropping are: (a)
> to do a Wald test to test whether the controls are simultaneously different
> from zero; (b) to do a Hausman test comparing the consistent and efficient
> estimator or a Chow test for the common regressors. See the first series of
> discussions (eq. 2-5c to see that consistency is never harmed even if a
> omitted regressor is not a significant predictor: Antonakis, J., Bendahan,
> S., Jacquart, P., & Lalive, R. 2010. On making causal claims: A review and
> recommendations. The Leadership Quarterly, 21(6): 1086-1120.
> http://www.hec.unil.ch/jantonakis/Causal_Claims.pdf
>
> Best,
> J.
>
> __________________________________________
>
> John Antonakis
> Professor of Organizational Behavior
> Director, Ph.D. Program in Management
>
> Faculty of Business and Economics
> University of Lausanne
> Internef #618
> CH-1015 Lausanne-Dorigny
> Switzerland
> Tel ++41 (0)21 692-3438
> Fax ++41 (0)21 692-3305
> http://www.hec.unil.ch/people/jantonakis
>
> Associate Editor
> The Leadership Quarterly
> __________________________________________
>
>
> On 07.03.2013 08:07, James Bernard wrote:
>>
>> Hi all,
>>
>> I have a question which may sound too basic, but I wonder if anyone could
>> help:
>>
>> We often add control variables that turn out to be insignificant. Does
>> that mean that I can remove that variable form my model without being
>> concerned with omitted variable bias?
>>
>> Thanks,
>> James
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