Many macroeconomic time series are characterised by long periods of positive growth, expansion periods, and short periods of negative growth, recessions. A popular model to describe this phenomenon is the Markov trend, which is a stochastic segmented trend where the slope depends on the value of an unobserved two-state first-order Markov process. The two slopes of the Markov trend describe the growth rates in the two phases of the business cycle. This thesis deals with a Bayesian analysis of univariate and multivariate macroeconomic time series using Markov trend models. We consider Bayesian methods to analyse the presence of stochastic trends and the business cycle.

Additional Metadata
Keywords Markov processes, methods and techniques, time series
Promotor H.K. van Dijk (Herman)
Publisher Erasmus University Rotterdam , Thela Thesis, Amsterdam
Sponsor Dijk, Prof. Dr. H.K. van (promotor)
Persistent URL hdl.handle.net/1765/2043
Series Tinbergen Instituut Research Series
Citation
Paap, R. (1997, November 27). Markov Trends in Macroeconomic Time Series (No. 171). Tinbergen Instituut Research Series. Thela Thesis, Amsterdam. Retrieved from http://hdl.handle.net/1765/2043

Additional Files
dissert.zip Final Version , 1mb