Optimal Tests for Nested Model Selection with Underlying Parameter Instability
AbstractThis paper develops optimal tests for model selection between two nested models in the presence of underlying parameter instability. These are joint tests for both parameter instability and a null hypothesis on (a subset of) the parameters. They modify the existing tests for parameter instability to allow the parameter vector to be unknown. It is commonly argued that out-of-sample rolling tests are useful to select between competing models when the parameters are time-varying. This paper argues that the optimal tests identified here are locally asymptotically more powerful than the out-of-sample rolling tests. It also shows that the optimal tests are more powerful than sequential tests that test for parameter instability in a first stage and select the model in a second state, the reason being that the two stages of the test are not independent. A simple empirical application to international finance models of nominal exchange rate determination is considered.
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Bibliographic InfoPaper provided by Duke University, Department of Economics in its series Working Papers with number 02-05.
Date of creation: 2002
Date of revision:
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Other versions of this item:
- Rossi, Barbara, 2005. "Optimal Tests For Nested Model Selection With Underlying Parameter Instability," Econometric Theory, Cambridge University Press, vol. 21(05), pages 962-990, October.
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
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