This research was supported in part by grants from the Open Society Foundations and the Oxford Martin School.
Model Selection in Equations with Many ‘Small’ Effects*
Article first published online: 8 OCT 2012
© Blackwell Publishing Ltd and the Department of Economics, University of Oxford 2012
Oxford Bulletin of Economics and Statistics
Special Issue: Large Data Sets
Volume 75, Issue 1, pages 6–22, February 2013
How to Cite
Castle, J. L., Doornik, J. A. and Hendry, D. F. (2013), Model Selection in Equations with Many ‘Small’ Effects. Oxford Bulletin of Economics and Statistics, 75: 6–22. doi: 10.1111/j.1468-0084.2012.00727.x
- Issue published online: 21 DEC 2012
- Article first published online: 8 OCT 2012
- Final Manuscript Received: August 2012
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.