Cyclical components in economic time series are analysed in a Bayesian framework, thereby allowing prior notions about periodicity to be used. The method is based on a general class of unobserved component models that allow relatively smooth cycles to be extracted. Posterior densities of...

A general Bayesian Markov Chain Monte Carlo methodology is utilized for conducting an analysis of the intensity process of stock market data. The sampling scheme employed is a hybrid of the Gibbs and Metropolis Hastings algorithms. Both duration and count data time series approaches are utilized...

Economic policy decisions are often informed by empirical economic analysis. While the decision-maker is usually only interested in good estimates of outcomes, the analyst is interested in estimating the model. Accurate inference on the structural features of a model, such as cointegration, can...

There is considerable disagreement in the empirical macro literature as to the degree of returns to scale in U.S. production. While many studies find evidence of a small degree of increasing returns, standard errors are typically large. This issue is of importance for assessing the possibility...

This paper proposes a Structural Error Correction Model (SECM) that allows concurrent estimation of the structural parameters and analysis of cointegration. We amalgamate the Bayesian methods of Kleibergen and Paap (2002) for analysis of cointegration in the ECM, and the Bayesian methods of...

We consider the estimation of a large number of GARCH models, say of the order of several hundreds. Especially in the multivariate case, the number of parameters is extremely large. To reduce this number and render estimation feasible, we regroup the series in a small number of clusters. Within...

We examine the equity market price interdependence between Australia, on one hand, and Japan, US, UK, Hong Kong, Singapore, Taiwan and Korea, on the other hand, based on Hacker and Hatemi-J (2003) bootstrap causality tests with leveraged adjustments. We cover the period January 1, 1993 to...

Kernel density estimation for multivariate data is an important technique that has a wide range of applications in econometrics and finance. However, it has received significantly less attention than its univariate counterpart. The lower level of interest in multivariate kernel density...

This paper presents Bayesian methodology for the estimation of a bivariate probit model with an endogenous effect and both parametric linear and flexible semiparametric exogenous effects. The model is prompted by an analysis of the utilisation of health services in Australia using data from the...

Recent empirical evidence suggests that the weekend and holiday calendar effects are much stronger and statistically significant in volatility as opposed to expected returns. This paper seeks an explanation for this empirical finding by undertaking a comprehensive investigation of the predictive...