The usual index of leading indicators has constant weights on its components and is therefore implicitly premised on the assumption that the dynamical properties of the economy remain the same over time and across phases of the business cycle. We explore the possibility that the business cycle...

Greene (2002, 2004) examines several extensions of the panel stochastic frontier models including what he calls the “true†fixed and random effect stochastic frontier models. In this paper we extend these two models to their semiparametric alternatives where the functional form for...

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...

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...

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...

Under a Bayesian framework of model uncertainty, closed economy models of monetary policy typically suggest that policy responses should be attenuated. Conversely, under a Knightian view of uncertainty, where the policymaker cannot specify probabilities across alternative models, intensifying...