Optimal Tests for Nested Model Selection with Underlying Parameter Instability

Barbara Rossi

No 02-05, Working Papers from Duke University, Department of Economics

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.

JEL-codes: C52 C53 (search for similar items in EconPapers)
Date: 2002
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Citations: View citations in EconPapers (9)

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Related works:
Journal Article: OPTIMAL TESTS FOR NESTED MODEL SELECTION WITH UNDERLYING PARAMETER INSTABILITY (2005)
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