A Bayesian Analysis of Unit Roots and Structural Breaks in the Level and the Error Variance of Autoregressive Models
AbstractIn this paper, a Bayesian approach is suggested to compare unit root models with stationary models when both the level and the error variance are subject to structural changes (known as breaks) of an unknown date. The paper utilizes analytic and Monte Carlo integration techniques for calculating the marginal likelihood of the models under consideration, in order to compute the posterior model probabilities. The performance of the method is assessed by simulation experiments. Some empirical applications of the method are conducted with the aim to investigate if it can detect structural breaks in financial series, with changes in the error variance.
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Bibliographic InfoPaper provided by Queen Mary, University of London, School of Economics and Finance in its series Working Papers with number 514.
Date of creation: Jul 2004
Date of revision:
Bayesian inference; Model comparison; Autoregressive models; Unit roots; Structural breaks;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
This paper has been announced in the following NEP Reports:
- NEP-ALL-2004-08-02 (All new papers)
- NEP-ECM-2004-08-09 (Econometrics)
- NEP-ETS-2004-08-02 (Econometric Time Series)
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