- 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
More Variables than Observations
More Variables than Observations
- Chapter:
- (p.233) 19 More Variables than Observations
- Source:
- Empirical Model Discovery and Theory Evaluation
- Author(s):
David F. Hendry
- Publisher:
- The MIT Press
We now move to also using Autometrics as a new way of thinking about a range of problems previously deemed almost intractable. In this part, we consider five major areas. First, the approach of impulse-indicator saturation leads in this chapter to ways of handling excess numbers of variables, N > T, based on a mixture of reduction and expansion steps, with an empirical illustration. Secondly, IIS also allows the investigation of multiple breaks, addressed in chapter 20. Third, chapter 21 considers selecting nonlinear models, which raise some new issues, and also often involve more candidate variables than observations. In turn, applying IIS to detect breaks in models of marginal processes, then testing the relevance of the retained indicators in conditional equations leads to a new test for super exogeneity, described in chapter 22. Finally, chapter 23 discusses selecting forecasting models. Throughout, we use automatic model selection as a new instrument, which changes how to think about existing problems, and suggests novel solutions.
Keywords: More variables than observations, reduction and expansion steps, Hoover–Perez
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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
