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Christoph Hanck
 
''I just ran two trillion regressions''
( 2016, Vol. 36 No.4 )
 
 
The computational effort required to conduct a full model search to identify the most useful specification in problems that feature a large set of potential explanatory variables is widely perceived to be large. To circumvent or mitigate this challenge, the literature has proposed a host of techniques, many of which are not easy to implement. Using the example of a standard cross-country growth regression data set, we demonstrate that the computational effort in conducting a full model search will often be negligible. We provide an assessment of how this finding generalizes to model spaces of different sizes.
 
 
Keywords: Variable selection, growth regressions, branch and bound, best subset selection
JEL: C1 - Econometric and Statistical Methods: General
O4 - Economic Growth and Aggregate Productivity: General
 
Manuscript Received : Apr 05 2016 Manuscript Accepted : Nov 09 2016

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