Bayesian Estimation and Model Selection for the Weekly Colombian Exchange Rate

Publicado en

  • Revista de Economía del Rosario


  • This document reviews and applies recently developed techniques for Bayesian estimation and model selection in the context of Time Series modelingfor Stochastic volatility. After the literature review on Generalized Conditional Autoregressive models, Stochastic VolatiliTY models, and the relevant results on Markov chain monte Carlo methods (MCMC), an example applying such techniques is shown. The methodology is used with a series of Weekly Colombian - USA Exchange Rate on seven different models. The GARCH model, which uses Type-IV Pearson distribution, is favored for the selecting technique, Reversible Jump MCMC, over other models, including stochastic VolatiliTY models with a student-t distribution.

fecha de publicación

  • 2001

Líneas de investigación

  • Bayesian Estimation
  • GARCH Models
  • Markov Hain Monte Carlo Methods (MCMC)
  • Model Selection
  • Stochastic Volatility