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Non-linear prediction in the presence of macroeconomic regimes
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This paper studies the predictive performance and in-sample dynamics of three regime switching models for Swedish macroeconomic time series. The models discussed are threshold autoregressive (TAR), Markov switching autoregressive (MSM-AR), and smooth-transition autoregressive (STAR) regime switching models. We perform recursive out-of-sample forecasting to study the predictive performance of the models. We also assess the in-sample dynamics correspondence to the forecast performance and find that there is not always a relationship. Furthermore, we seek to explore if these unrestricted models yield interpretable results regarding the regimes from an macroeconomic standpoint. We assess GDP-growth, the unemployment rate, and government bond yields and find evidence of Teräsvirta's claims that even when the data has non-linear dynamics, non-linear models might not improve the forecast performance of linear models when the forecast window is linear.

Place, publisher, year, edition, pages
Keyword [en]
Markov Switching, Regime Switching, Smooth-transition, Time-varying parameters, Threshold model
National Category
Probability Theory and Statistics
URN: urn:nbn:se:uu:diva-297222OAI: diva2:941301
Subject / course
Educational program
Master Programme in Statistics
Available from: 2016-06-22 Created: 2016-06-22 Last updated: 2016-06-22Bibliographically approved

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