- 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
Testing Super Exogeneity
Testing Super Exogeneity
- Chapter:
- (p.263) 22 Testing Super Exogeneity
- Source:
- Empirical Model Discovery and Theory Evaluation
- Author(s):
David F. Hendry
- Publisher:
- The MIT Press
An automatically computable test is described, with null rejection frequencies that are close to the nominal size, and potency for failures of super exogeneity. Impulse-indicator saturation is undertaken in the marginal models of the putative exogenous variables that enter the conditional model contemporaneously, and all significant outcomes are recorded. These indicators from the marginal models are added to the conditional model and tested for significance. Under the null of super exogeneity, the test has the correct gauge for a range of sizes of marginal-model saturation tests, both when those processes are constant, and when they undergo shifts in either mean or variance. Failures of super exogeneity from a violation of weak exogeneity are shown to be detectable when there are location shifts in the marginal models. The distribution and potency of the test are derived and simulated, with an application to testing super exogeneity for UK consumers’ expenditure.
Keywords: Super exogeneity, Invariance, marginal models, 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
