Revisiting the transitional dynamics of business-cycle phases with mixed frequency data

Marie Bessec
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Marie Bessec: LEDa - Laboratoire d'Economie de Dauphine - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres

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Abstract: This paper introduces a Markov-Switching model where transition probabilities depend on higher frequency indicators and their lags, through polynomial weighting schemes. The MSV-MIDAS model is estimated via maximum likel ihood methods. The estimation relies on a slightly modified version of Hamilton's recursive filter. We use Monte Carlo simulations to assess the robustness of the estimation procedure and related test-statistics. The results show that ML provides accurate estimates, but they suggest some caution in the tests on the parameters involved in the transition probabilities. We apply this new model to the detection and forecast of business cycle turning points. We properly detect recessions in United States and United Kingdom by exploiting the link between GDP growth and higher frequency variables from financial and energy markets. Spread term is a particularly useful indicator to predict recessions in the United States, while stock returns have the strongest explanatory power around British turning points.

Keywords: Markov-Switching; mixed frequency data; business cycles (search for similar items in EconPapers)
Date: 2015-06-22
New Economics Papers: this item is included in nep-ecm, nep-for and nep-mac
Note: View the original document on HAL open archive server: https://hal.science/hal-01276824v1
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Published in AFSE 2015 64th Congress of the French Economic Association, Jun 2015, Rennes, France. pp.34

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