Revisiting the Transitional Dynamics of Business-Cycle Phases with Mixed Frequency Data
提出一种马尔可夫转换模型，利用高频指标及其滞后项通过多项式加权影响转移概率，用于预测美国商业周期转折点，并发现金融和能源市场的高频变量有助于识别衰退。
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.