Using Model Selection Algorthims to Obtain Reliable Coefficient Estimates
AbstractThis review surveys a number of common Model Selection Algorithms (MSAs), discusses how they relate to each other, and identifies factors that explain their relative performances. At the heart of MSA performance is the trade-off between Type I and Type II errors. Some relevant variables will be mistakenly excluded, and some irrelevant variables will be retained by chance. A successful MSA will find the optimal trade-off between the two types of errors for a given data environment. Whether a given MSA will be successful in a given environment depends on the relative costs of these two types of errors. We use Monte Carlo experimentation to illustrate these issues. We confirm that no MSA does best in all circumstances. Even the worst MSA in terms of overall performance – the strategy of including all candidate variables – sometimes performs best (viz., when all candidate variables are relevant). We also show how (i) the ratio of relevant to total candidate variables and (ii) DGP noise affect relative MSA performance. Finally, we discuss a number of issues complicating the task of MSAs in producing reliable coefficient estimates.
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Bibliographic InfoPaper provided by University of Canterbury, Department of Economics and Finance in its series Working Papers in Economics with number 11/03.
Length: 51 pages
Date of creation: 01 Jan 2011
Date of revision:
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Model selection algorithms; Information Criteria; General-to-Specific modeling; Bayesian Model Averaging; Portfolio Models; AIC; SIC; AICc; SICc; Monte Carlo Analysis; Autometrics;
Other versions of this item:
- Jennifer L. Castle & Xiaochuan Qin & W. Robert Reed, 2013. "Using Model Selection Algorithms To Obtain Reliable Coefficient Estimates," Journal of Economic Surveys, Wiley Blackwell, vol. 27(2), pages 269-296, 04.
- 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
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