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Optimal Tests For Nested Model Selection With Underlying Parameter Instability

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  • Rossi, Barbara

Abstract

This 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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  • 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.
  • Handle: RePEc:cup:etheor:v:21:y:2005:i:05:p:962-990_05
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    JEL classification:

    • 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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