- 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
Model Selection in Underspecified Settings
Model Selection in Underspecified Settings
- Chapter:
- (p.223) 18. Model Selection in Underspecified Settings
- Source:
- Empirical Model Discovery and Theory Evaluation
- Author(s):
David F. Hendry
- Publisher:
- The MIT Press
Despite seeking to commence an empirical study from a general initial specification that nests the LDGP for the set of variables under analysis, the GUM may be an underspecification. Moreover, the selection of the variables to analyze could lead to a poor representation of the economic data generation process. In this setting, model selection, rather than just fitting a prior specification, may help. Impulse-indicator saturation can correct nonconstancies induced by location shifts in omitted variables that alter the intercepts of models. Since IIS is a robust estimation method, it can mitigate some of the adverse effects of induced location shifts when models are mis-specified. The chapter provides an analysis of a simple setting, a Monte Carlo study thereof, and an artificial data example to illustrate.
Keywords: Underspecified models, non-constant coefficients, impulse-indicator saturation
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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
