An efficiency Bayesian unit root test in Unobserved-ARCH models

ABSTRACT

This paper investigates the new prior distribution on the Unobserved-Autoregressive Conditional Heteroscedasticity (ARCH) unit root test. Monte Carlo simulations show that the sample size is seriously effective in efficiency of Bayesian test. To improve the performance of Bayesian test for unit root, we propose a new Bayesian test that is robust in the presence of stationary and nonstationary Unobserved-ARCH. The finite sample property of the proposed test statistic is evaluated using Monte Carlo studies. Applying the developed method, we test the policy of daily exchange rate of the German Marc with respect to the Greek Drachma.

KEYWORDS:

• Bayes factor
• Gibbs sampling
• Monte Carlo Markov Chain
• Unit root
• Unobserved-ARCH models

MATHEMATICS SUBJECT CLASSIFICATION:

• 62F15
• 91B84
• 62P20

Acknowledgment

The authors want to thank an anonymous referee for very constructive comments that greatly improved the paper. Also, they thank the Persian Gulf university for financial support.