Model Selection in Equations with Many 'Small' Effects

Jennifer Castle, Jurgen Doornik and David Hendry

Working Paper series from Rimini Centre for Economic Analysis

Abstract: High dimensional general unrestricted models (GUMs) may include important individual determinants, many small relevant effects, and irrelevant variables. Automatic model selection procedures can handle 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, possibly representing 'factor' structures, as perfect collinearity is also unproblematic. 'Factors' can capture small influences that selection may not retain individually. 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; Monte Carlo (search for similar items in EconPapers)
JEL-codes: C22 C52 (search for similar items in EconPapers)
Date: 2012-07
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Citations: View citations in EconPapers (2)

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Related works:
Journal Article: Model Selection in Equations with Many ‘Small’ Effects (2013)
Working Paper: Model Selection in Equations with Many 'Small' Effects (2011)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:53_12

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