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Model Selection in Equations with Many ‘Small’ Effects


  • This research was supported in part by grants from the Open Society Foundations and the Oxford Martin School.


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, nonlinear 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.