Abstract (EN)

This paper introduces a Markov-switching model in which transition probabilities depend on higher frequency indicators and their lags through polynomial weighting schemes. The MSV-MIDAS model is estimated through maximum likelihood (ML) methods with a slightly modified version of Hamilton’s filter. Monte Carlo simulations show that ML provides accurate estimates, but they suggest some caution in interpreting the tests of the parameters in the transition probabilities. We apply this new model to forecast business cycle turning points in the United States. We properly detect recessions by exploiting the link between GDP growth and higher frequency variables from financial and energy markets.