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.

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Dijk, Prof. Dr. H.K. van (promotor)
H.K. van Dijk (Herman)
Erasmus University Rotterdam , Thela Thesis, Amsterdam
hdl.handle.net/1765/2043
Tinbergen Instituut Research Series
Erasmus School of Economics

Paap, R. (1997, November 27). Markov Trends in Macroeconomic Time Series (No. 171). Tinbergen Instituut Research Series. Retrieved from http://hdl.handle.net/1765/2043

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