Statistical Model Selection with 'Big Data'
David Hendry () and
No 735, Economics Series Working Papers from University of Oxford, Department of Economics
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
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Journal Article: Statistical model selection with “Big Data” (2015)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:oxf:wpaper:735
Access Statistics for this paper
More papers in Economics Series Working Papers from University of Oxford, Department of Economics Contact information at EDIRC.
Bibliographic data for series maintained by Anne Pouliquen ().