Skip to main content
Article thumbnail
Location of Repository

A Monte Carlo comparison of Bayesian testing for cointegration rank

By Katsuhiro Sugita

Abstract

This article considers a Bayesian testing for cointegration rank, using an approach developed by Strachan and van Dijk (2007), that is based on Koop, Leon-Gonzalez, and Strachan (2006). The Bayes factors are calculated for selecting cointegrating rank. We calculate the Bayes factors using two methods - the Schwarz BIC approximation and Chib's (1995) algorithm for calculating the marginal likelihood. We run Monte Carlo simulations to compare the two methods.

OAI identifier:

Suggested articles

Citations

  1. (2000). A Bayesian Time Series Model of Multiple Structural Changes in
  2. (2007). Bayesian Analysis of a Vector Autoregressive Model with Multiple Structural
  3. (2004). Bayesian Analysis of the Error Correction Model”
  4. (2007). Bayesian Model Averaging in Vector Autoregressive
  5. (2006). Bayesian Point Estimation of the Cointegration Space”
  6. (2005). Bayesian Reference Analysis of
  7. (1995). Computing Bayes Factors Using a Generalization of the Savage-Dickey Density Ratio”
  8. (1994). Covariance Structure of the Gibbs Sampler with Application to
  9. (2006). Efficient Posterior Simulation for Cointegrated Models with Priors on the Cointegration Space”
  10. (1978). Estimating the Dimension of a Model”
  11. (1995). Marginal Likelihood from the Gibbs Output”
  12. (2006). OX an Object-Oriented Matrix Programming Language. London: Timberlake Consultants Press.
  13. (1994). The Collapsed Gibbs Sampler with Application to a Gene Regulation Problem”
  14. (2003). Valid Bayesian Estimation of the Cointegrating Error Correction Model”

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.