- Title Pages
- About the Arne Ryde Foundation
- Preface
- Acknowledgments
- Glossary
- Data and Software
-
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 -
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 -
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
Role of Encompassing
Role of Encompassing
- Chapter:
- (p.159) 13 Role of Encompassing
- Source:
- Empirical Model Discovery and Theory Evaluation
- Author(s):
David F. Hendry
- Publisher:
- The MIT Press
Encompassing seeks to reconcile competing empirical models, all of which claim to explain some economic phenomena. If distinct competing models exist, all but one must either be incomplete or incorrect—and all may be false. By testing whether one model can account for the results found by the other models, investigators can learn how well their model performs relative to those, as well as reduce the class of admissible models. Some features of the LDGP may not be included, so different empirical models capture different sets of salient features. All empirical models are encompassed by the LDGP, in that knowledge of that LDGP allows one to account for all the findings reported, even when some models are not nested with respect to others. It is natural to seek models which encompass the LDGP, and that motivates our selection strategy: specify a GUM which nests the LDGP, then simplify it to a model that is as parsimonious as feasible while still encompassing the GUM.
Keywords: Encompassing, rival models, parsimonious encompassing, backward elimination
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- Title Pages
- About the Arne Ryde Foundation
- Preface
- Acknowledgments
- Glossary
- Data and Software
-
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 -
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 -
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
