The VAR-VARCH model: A Bayesian approach

In this paper, we develop a combined Bayesian vector autoregressive and conditional heteroskedasticity (VAR--VARCH) models. A Gibbs sampling approach is suggested for the univariate and multivariate VAR--VARCH models. Using a random coefficient formulation it is shown that full conditional distributions are derived in closed analytical forms. The method is applied to monthly exchange rate data, Swiss Francs and Deustch Marks to U.S. Dollars. Keywords: exchange rates, full conditional distributions, Gibbs sampling, random coefficients, VAR--VARCH models. 1 Introduction ARCH (autoregressive conditional heteroskedasticity) models have obtained considerably attention in the analysis of financial time series since their introduction by Engle (1982). They are used to capture the tendency for volatility clustering, i.e., for the tendency of large (small) price changes to be followed by other large (small) price changes. There are now more than several hundred papers discussing theoretical ..

Testing for negativity in a demand system: A Bayesian approach

The paper introduces Bayesian inference into a demand model. This allows us to test for the negativity condition of the substitution matrix which is difficult to handle directly in the traditional approach. To illustrate the Bayesian inference procedures, we estimate the Rotterdam model and test the demand properties using Japanese data. The empirical results show the importance of specifically considering negativity in demand analysis.Markov chain Monte Carlo (MCMC) · conditional predictive ordinate (CPO) · Bayes factor · Rotterdam model