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Statistical Model Selection with 'Big Data'

David Hendry () and Jurgen Doornik

No 735, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: Big Data offer potential benefits for statistical modelling, but confront problems like an excess of false positives, mistaking correlations for causes, ignoring sampling biases, and selecting by inappropriate methods. We consider the many important requirements when searching for a data-based relationship using Big Data, and the possible role of Autometrics in that context. Paramount considerations include embedding relationships in general initial models, possibly restricting the number of variables to be selected over by non-statistical criteria (the formulation problem), using good quality data on all variables, analyzed with tight significance levels by a powerful selection procedure, retaining available theory insights (the selection problem) while testing for relationships being well specified and invariant to shifts in explanatory variables (the evaluation problem), using a viable approach that resolves the computational problem of immense numbers of possible models.

Keywords: Big Data; Model Selection; Location Shifts; Autometrics (search for similar items in EconPapers)
JEL-codes: C51 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2014-12-09
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