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...

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...

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...

This paper is concerned with Bayesian reduced rank regression when instruments are weak. There have been a number of studies on weak identification problem with the application of reduced rank regression combined with singular value decomposition (SVD) method in the Bayesian framework, see...

This study addresses the complexity in modeling contingent valuation surveys with true zeros and non-ignorable missing responses including “don’t knows†and protest responses. An endogenous switching tobit model is specified to simultaneously estimate the parameters of the...

Markov switching GARCH models have been developed in order to address the statistical regularity observed in financial time series such as strong persistence of conditional variance. However, Maximum Likelihood Estimation faces a implementation problem since the conditional variance depends on...