Model Selection in Equations with Many 'Small' Effects

Jennifer Castle and David Hendry
Authors registered in the RePEc Author Service: Jurgen A. Doornik

No 528, Economics Series Working Papers from University of Oxford, Department of Economics

Abstract: General unrestricted models (GUMs) may include important individual determinants, many small relevant effects, and irrelevant variables. Automatic model selection procedures can handle perfect collinearity and more candidate variables than observations, allowing substantial dimension reduction from GUMs with salient regressors, lags, non-linear transformations, and multiple location shifts, together with all the principal components representing 'factor' structures, which can also capture small influences that selection may not retain individually. High dimensional GUMs and even the final model can implicitly include more variables than observations entering via 'factors'. We simulate selection in several special cases to illustrate.

Keywords: Model selection; high dimensionality; principal components; non-linearity; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C22 C51 (search for similar items in EconPapers)
Date: 2011-02-01
New Economics Papers: this item is included in nep-cba, nep-cis, nep-ecm, nep-ets and nep-ore
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://ora.ox.ac.uk/objects/uuid:df2b1bda-7589-40bf-9668-35025dd3c311 (text/html)

Related works:
Journal Article: Model Selection in Equations with Many ‘Small’ Effects (2013)
Working Paper: Model Selection in Equations with Many 'Small' Effects (2012)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oxf:wpaper:528

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 ( this e-mail address is bad, please contact ).