How To Pick The Best Regression Equation: A Review And Comparison Of Model Selection Algorithms
AbstractThis paper reviews and compares twenty-one different model selection algorithms (MSAs) representing a diversity of approaches, including (i) information criteria such as AIC and SIC; (ii) selection of a “portfolio” or best subset of models; (iii) general-to-specific algorithms, (iv) forward-stepwise regression approaches; (v) Bayesian Model Averaging; and (vi) inclusion of all variables. We use coefficient unconditional mean-squared error (UMSE) as the basis for our measure of MSA performance. Our main goal is to identify the factors that determine MSA performance. Towards this end, we conduct Monte Carlo experiments across a variety of data environments. Our experiments show that MSAs differ substantially with respect to their performance on relevant and irrelevant variables. We relate this to their associated penalty functions, and a bias-variance tradeoff in coefficient estimates. It follows that no MSA will dominate under all conditions. However, when we restrict our analysis to conditions where automatic variable selection is likely to be of greatest value, we find that two general-to-specific MSAs, Autometrics, do as well or better than all others in over 90% of the experiments.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 09/13.
Length: 55 pages
Date of creation: 01 Oct 2009
Date of revision:
Contact details of provider:
Postal: Private Bag 4800, Christchurch, New Zealand
Phone: 64 3 369 3123 (Administrator)
Fax: 64 3 364 2635
Web page: http://www.econ.canterbury.ac.nz
More information through EDIRC
Model selection algorithms; Information Criteria; General-to-Specific modeling; Bayesian Model Averaging; Portfolio Models; AIC; SIC; AICc; SICc; Monte Carlo Analysis; Autometrics;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
This paper has been announced in the following NEP Reports:
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Steven L. Scott & Hal R. Varian, 2013.
"Bayesian Variable Selection for Nowcasting Economic Time Series,"
NBER Working Papers
19567, National Bureau of Economic Research, Inc.
- Steven L. Scott & Hal Varian, 2013. "Bayesian Variable Selection for Nowcasting Economic Time Series," NBER Chapters, in: Economics of Digitization National Bureau of Economic Research, Inc.
- Castle Jennifer L. & Doornik Jurgen A & Hendry David F., 2011.
"Evaluating Automatic Model Selection,"
Journal of Time Series Econometrics,
De Gruyter, vol. 3(1), pages 1-33, February.
- Graham Bird & Alex Mandilaras & Helen Popper, 2012. "Explaining Shifts in Exchange Rate Regimes," School of Economics Discussion Papers 1312, School of Economics, University of Surrey.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Albert Yee).
If references are entirely missing, you can add them using this form.