A new posterior odds analysis is proposed to test for a unit root in volatility dynamics in the context of stochastic volatility models. This analysis extends the Bayesian unit root test of So and Li (1999, Journal of Business Economic Statistics) in two important ways. First, a numerically more stable algorithm is introduced to compute the Bayes factor, taking into account the special structure of the competing models. Owing to its numerical stability, the algorithm overcomes the problem of diverged “size” in the marginal likelihood approach. Second, to improve the “power” of the unit root test, a mixed prior specification with random weights is employed. It is shown that the posterior odds ratio is the by-product of Bayesian estimation and can be easily computed by MCMC methods. A simulation study examines the “size” and “power” performances of the new method. An empirical study, based on time series data covering the subprime crisis, reveals some interesting results.
Bayes factor, Mixed Prior, Markov Chain Monte Carlo, Posterior odds ratio, Stochastic volatility models, Unit root testing.
Econometrics | Economic Theory
LI, Yong and YU, Jun.
A New Bayesian Unit Root Test in Stochastic Volatility Models. (2010). Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1240
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