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.
