Is it valid to iterate over every permutation of a regression specification and compute an “average...












1












$begingroup$


I had an idea, and was wondering if it's ever done and, if so, how to do it in an appropriate manner.



Let's say I run an OLS model and the results come back significant. There is discussion as to whether the positive results hold up given a different set of control variables. However, it's theoretically unclear which control variables are actually relevant, and we don't just want to throw in the kitchen sink.



Let's say it's computationally feasible to run every single model (with every possible permutation of control variables), and we collect the coefficient and standard error of the coefficient of interest in each. We can report the proportion of models where this coefficient is significant, but is there some kind of meta-test that can be done showing that the test statistic is significant in a significant number of alternative specifications? Is this ever done?






regression least-squares







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  • $begingroup$
    I don't have the time to write up a full explanation right now, but you may want to look into Leamer bounds
    $endgroup$
    – duckmayr
    1 hour ago
















1












$begingroup$


I had an idea, and was wondering if it's ever done and, if so, how to do it in an appropriate manner.



Let's say I run an OLS model and the results come back significant. There is discussion as to whether the positive results hold up given a different set of control variables. However, it's theoretically unclear which control variables are actually relevant, and we don't just want to throw in the kitchen sink.



Let's say it's computationally feasible to run every single model (with every possible permutation of control variables), and we collect the coefficient and standard error of the coefficient of interest in each. We can report the proportion of models where this coefficient is significant, but is there some kind of meta-test that can be done showing that the test statistic is significant in a significant number of alternative specifications? Is this ever done?






regression least-squares







share|cite|improve this question









$endgroup$












  • $begingroup$
    I don't have the time to write up a full explanation right now, but you may want to look into Leamer bounds
    $endgroup$
    – duckmayr
    1 hour ago














1












1








1


1



$begingroup$


I had an idea, and was wondering if it's ever done and, if so, how to do it in an appropriate manner.



Let's say I run an OLS model and the results come back significant. There is discussion as to whether the positive results hold up given a different set of control variables. However, it's theoretically unclear which control variables are actually relevant, and we don't just want to throw in the kitchen sink.



Let's say it's computationally feasible to run every single model (with every possible permutation of control variables), and we collect the coefficient and standard error of the coefficient of interest in each. We can report the proportion of models where this coefficient is significant, but is there some kind of meta-test that can be done showing that the test statistic is significant in a significant number of alternative specifications? Is this ever done?






regression least-squares







share|cite|improve this question









$endgroup$




I had an idea, and was wondering if it's ever done and, if so, how to do it in an appropriate manner.



Let's say I run an OLS model and the results come back significant. There is discussion as to whether the positive results hold up given a different set of control variables. However, it's theoretically unclear which control variables are actually relevant, and we don't just want to throw in the kitchen sink.



Let's say it's computationally feasible to run every single model (with every possible permutation of control variables), and we collect the coefficient and standard error of the coefficient of interest in each. We can report the proportion of models where this coefficient is significant, but is there some kind of meta-test that can be done showing that the test statistic is significant in a significant number of alternative specifications? Is this ever done?






regression least-squares




regression least-squares






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asked 6 hours ago









ParseltongueParseltongue

267114




267114












  • $begingroup$
    I don't have the time to write up a full explanation right now, but you may want to look into Leamer bounds
    $endgroup$
    – duckmayr
    1 hour ago


















  • $begingroup$
    I don't have the time to write up a full explanation right now, but you may want to look into Leamer bounds
    $endgroup$
    – duckmayr
    1 hour ago
















$begingroup$
I don't have the time to write up a full explanation right now, but you may want to look into Leamer bounds
$endgroup$
– duckmayr
1 hour ago




$begingroup$
I don't have the time to write up a full explanation right now, but you may want to look into Leamer bounds
$endgroup$
– duckmayr
1 hour ago










2 Answers
2






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$begingroup$

One similar practice is called model averaging. You fit many models, make many predictions, and average the results, weighting each by the posterior probability that the model is correct. There's a nice introduction here.



https://www2.stat.duke.edu/courses/Spring05/sta244/Handouts/press.pdf



I would advise you not to do this with coefficients, because it's not how BMA was meant to be used. The issue is that the same coefficient can have a different interpretation and target parameter in different models. Because of this, averaging two coefficients often doesn't make conceptual sense. Katharine Bannar explains via example: in a model for brain weight based on body size and gestation time, gestation time will have little association with brain weight once body size is already accounted for. If body size is left out of the model, the gestation coefficient would increase dramatically. There is more explanation here.



https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/eap.1419






share|cite|improve this answer









$endgroup$





















    0












    $begingroup$

    Bayesian Model Averaging (BMA) is a principled way to do something like what you are describing.



    In short, with BMA, you have a collection of models and weight them according to the likelihood of each model.






    share|cite|improve this answer









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      2 Answers
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      active

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      2 Answers
      2






      active

      oldest

      votes









      active

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      active

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      4












      $begingroup$

      One similar practice is called model averaging. You fit many models, make many predictions, and average the results, weighting each by the posterior probability that the model is correct. There's a nice introduction here.



      https://www2.stat.duke.edu/courses/Spring05/sta244/Handouts/press.pdf



      I would advise you not to do this with coefficients, because it's not how BMA was meant to be used. The issue is that the same coefficient can have a different interpretation and target parameter in different models. Because of this, averaging two coefficients often doesn't make conceptual sense. Katharine Bannar explains via example: in a model for brain weight based on body size and gestation time, gestation time will have little association with brain weight once body size is already accounted for. If body size is left out of the model, the gestation coefficient would increase dramatically. There is more explanation here.



      https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/eap.1419






      share|cite|improve this answer









      $endgroup$


















        4












        $begingroup$

        One similar practice is called model averaging. You fit many models, make many predictions, and average the results, weighting each by the posterior probability that the model is correct. There's a nice introduction here.



        https://www2.stat.duke.edu/courses/Spring05/sta244/Handouts/press.pdf



        I would advise you not to do this with coefficients, because it's not how BMA was meant to be used. The issue is that the same coefficient can have a different interpretation and target parameter in different models. Because of this, averaging two coefficients often doesn't make conceptual sense. Katharine Bannar explains via example: in a model for brain weight based on body size and gestation time, gestation time will have little association with brain weight once body size is already accounted for. If body size is left out of the model, the gestation coefficient would increase dramatically. There is more explanation here.



        https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/eap.1419






        share|cite|improve this answer









        $endgroup$
















          4












          4








          4





          $begingroup$

          One similar practice is called model averaging. You fit many models, make many predictions, and average the results, weighting each by the posterior probability that the model is correct. There's a nice introduction here.



          https://www2.stat.duke.edu/courses/Spring05/sta244/Handouts/press.pdf



          I would advise you not to do this with coefficients, because it's not how BMA was meant to be used. The issue is that the same coefficient can have a different interpretation and target parameter in different models. Because of this, averaging two coefficients often doesn't make conceptual sense. Katharine Bannar explains via example: in a model for brain weight based on body size and gestation time, gestation time will have little association with brain weight once body size is already accounted for. If body size is left out of the model, the gestation coefficient would increase dramatically. There is more explanation here.



          https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/eap.1419






          share|cite|improve this answer









          $endgroup$



          One similar practice is called model averaging. You fit many models, make many predictions, and average the results, weighting each by the posterior probability that the model is correct. There's a nice introduction here.



          https://www2.stat.duke.edu/courses/Spring05/sta244/Handouts/press.pdf



          I would advise you not to do this with coefficients, because it's not how BMA was meant to be used. The issue is that the same coefficient can have a different interpretation and target parameter in different models. Because of this, averaging two coefficients often doesn't make conceptual sense. Katharine Bannar explains via example: in a model for brain weight based on body size and gestation time, gestation time will have little association with brain weight once body size is already accounted for. If body size is left out of the model, the gestation coefficient would increase dramatically. There is more explanation here.



          https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/eap.1419







          share|cite|improve this answer












          share|cite|improve this answer



          share|cite|improve this answer










          answered 6 hours ago









          eric_kernfelderic_kernfeld

          2,9921726




          2,9921726

























              0












              $begingroup$

              Bayesian Model Averaging (BMA) is a principled way to do something like what you are describing.



              In short, with BMA, you have a collection of models and weight them according to the likelihood of each model.






              share|cite|improve this answer









              $endgroup$


















                0












                $begingroup$

                Bayesian Model Averaging (BMA) is a principled way to do something like what you are describing.



                In short, with BMA, you have a collection of models and weight them according to the likelihood of each model.






                share|cite|improve this answer









                $endgroup$
















                  0












                  0








                  0





                  $begingroup$

                  Bayesian Model Averaging (BMA) is a principled way to do something like what you are describing.



                  In short, with BMA, you have a collection of models and weight them according to the likelihood of each model.






                  share|cite|improve this answer









                  $endgroup$



                  Bayesian Model Averaging (BMA) is a principled way to do something like what you are describing.



                  In short, with BMA, you have a collection of models and weight them according to the likelihood of each model.







                  share|cite|improve this answer












                  share|cite|improve this answer



                  share|cite|improve this answer










                  answered 6 hours ago









                  Bryan KrauseBryan Krause

                  495210




                  495210






























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