Empirical Model Discovery and Theory Evaluation

Automatic Selection Methods in Econometrics
MIT Press
  • 1. Auflage
  • |
  • erschienen am 4. Juli 2014
  • |
  • 392 Seiten
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-262-32441-0 (ISBN)
Economic models of empirical phenomena are developed for a variety of reasons, the most obvious of which is the numerical characterization of available evidence, in a suitably parsimonious form. Another is to test a theory, or evaluate it against the evidence, still another is to forecast future outcomes. Building such models involves a multitude of decisions, and the large number of features that need to be taken into account can overwhelm the researcher. Automatic model selection, which draws on recent advances in computation and search algorithms, can create, and then empirically investigate, a vastly wider range of possibilities than even the greatest expert. In this book, leading econometricians David Hendry and Jurgen Doornik report on their several decades of innovative research on automatic model selection. After introducing the principles of empirical model discovery and the role of model selection, Hendry and Doornik outline the stages of developing a viable model of a complicated evolving process. They discuss the discovery stages in detail, considering both the theory of model selection and the performance of several algorithms. They describe extensions to tackling outliers and multiple breaks, leading to the general case of more candidate variables than observations. Finally, they briefly consider selecting models specifically for forecasting.
  • Englisch
  • Cambridge
  • |
  • USA
978-0-262-32441-0 (9780262324410)
0-262-32441-5 (0262324415)
weitere Ausgaben werden ermittelt
  • Intro
  • Contents
  • About the Arne Ryde Foundation
  • Preface
  • Acknowledgments
  • Glossary
  • Data and Software
  • I Principles of Model Selection
  • 1 Introduction
  • 2 Discovery
  • 3 Background to Automatic Model Selection
  • 4 Empirical Modeling Illustrated
  • 5 Evaluating Model Selection
  • 6 The Theory of Reduction
  • 7 General-to-specific Modeling
  • II Model Selection Theory and Performance
  • 8 Selecting a Model in One Decision
  • 9 The 2-variable DGP
  • 10 Bias Correcting Selection Effects
  • 11 Comparisons of 1-cut Selection with Autometrics
  • 12 Impact of Diagnostic Tests
  • 13 Role of Encompassing
  • 14 Retaining a Theory Model During Selection
  • 15 Detecting Outliers and Breaks Using IIS
  • 16 Re-modeling UK Real Consumers' Expenditure
  • 17 Comparisons of Autometrics with Other Approaches
  • 18 Model Selection in Underspecified Settings
  • III Extensions of Automatic Model Selection
  • 19 More Variables than Observations
  • 20 Impulse-indicator Saturation for Multiple Breaks
  • 21 Selecting Non-linear Models
  • 22 Testing Super Exogeneity
  • 23 Selecting Forecasting Models
  • 24 Epilogue
  • References
  • Author Index
  • Index

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