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| Christoph Hanck |
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| ''I just ran two trillion regressions'' |
| ( 2016, Vol. 36 No.4 ) |
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| 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. |
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| 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 |
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| Manuscript Received : Apr 05 2016 | | Manuscript Accepted : Nov 09 2016 |
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