Empirical Model Discovery and Theory Evaluation: Automatic Selection Methods in Econometrics
David F. Hendry and Jurgen A. Doornik
Abstract
Part I introduces the notion of empirical model discovery and the role of model selection therein, discusses criteria to evaluate the success of methods for selecting empirical models, and introduces general-to-specific (Gets) approaches and the theory of reduction. It outlines the stages needed to discover a viable model of a complicated evolving process, even when there are more candidate variables than observations. Part II discusses those stages in detail, considering both the theory of model selection and the performance of several algorithms. The focus is on why automatic Gets methods ca ... More
Part I introduces the notion of empirical model discovery and the role of model selection therein, discusses criteria to evaluate the success of methods for selecting empirical models, and introduces general-to-specific (Gets) approaches and the theory of reduction. It outlines the stages needed to discover a viable model of a complicated evolving process, even when there are more candidate variables than observations. Part II discusses those stages in detail, considering both the theory of model selection and the performance of several algorithms. The focus is on why automatic Gets methods can outperform experts, delivering high success rates with near unbiased estimation. The core is explaining how to retain theory models with unchanged parameter estimates when that theory is valid, yet discover improved empirical models when that theory is incomplete or incorrect.Part III describes extensions to tackling outliers and multiple shifts using impulse-indicator saturation and handling more candidate variables than observations. These developments allow automatic testing of exogeneity and selecting non-linear models jointly with tackling all the other complications. Finally, we consider selecting models for forecasting.
Keywords:
Empirical discovery,
theory evaluation,
model selection,
Autometrics
Bibliographic Information
| Print publication date: 2014 |
Print ISBN-13: 9780262028356 |
| Published to MIT Press Scholarship Online: January 2015 |
DOI:10.7551/mitpress/9780262028356.001.0001 |
Authors
Affiliations are at time of print publication.
David F. Hendry, author
Oxford University
Jurgen A. Doornik, author
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