In this paper we suggest a Bayesian approach for inferring stationary autoregressive models allowing for possible structural changes (known as breaks) in both the mean and the error variance of economic series occuring at unknown times. Efficient Bayesian inference for the unknown number and...

Missing data or missing values are a common phenomenon in applied panel data research and of great interest for panel data unit root testing. The standard approach in the literature is to balance the panel by removing units and/or trimming a common time period for all units. However, this...

This paper employes a parametric model of structural breaks in the mean of stock returns which allows them to be endogenously driven by large positive or negative stock market return shocks. These shocks can be taken to reflect important market announcements, monetary policy regime shifts and/or...

This paper evaluates the VaR forecasting performance of the Markov regime switching (MRS) based volatility models, allowing for EGARCH effects. As is argued in the literature, this extension of the MRS model model may improve its forecasting performance due to its ability to capture leverage...