Posterior Odds Testing for a Unit Root with Data-Based Model Selection

Peter Phillips and Werner Ploberger

No 1017, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, Yale University

Abstract: The Kalman filter is sued to derive updating equations for the Bayesian data density in discrete time linear regression models with stochastic regressors. The implied "Bayes model" has time varying parameters and conditionally heterogeneous error variances. A sigma-finite "Bayes model" measure is given and used to produce a new model selection criterion (PIC) and objective posterior odds tests for sharp null hypotheses like the presence of a unit root. Simulation results and an empirical application are reported. The simulations show that the new model selection criterion "PIC" works very well and is generally superior to the Schwarz criterion BIC even in stationary systems.

Keywords: Kalman filter; Bayesian data density; stochastic regressors (search for similar items in EconPapers)
JEL-codes: C11 C51 C52 C53 (search for similar items in EconPapers)
Pages: 35 pages
Date: 1992-05
Note: CFP 878.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Published in Econometric Theory (1994), 10: 774-808

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Journal Article: Posterior Odds Testing for a Unit Root with Data-Based Model Selection (1994)
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