Business Cycle Turning Points : Mixed-Frequency Data with Structural Breaks

This papers develops a dynamic factor models with regime switching to account for the decreasing volatility of the U.S. economy observed since the mid-1980s. Apart from the Markov switching capturing the cyclical fluctuations, an additional type of regime switching is introduced to allow variances to switch between distinct regimes. The resulting four-regime models extend univariate analysis currently used in the literature on the structural break in conditional volatility to the multivariate time series. Besides the dynamic factor model using the data with a single (monthly) frequency, we employ the additonal information incorporating the mixed-frequency data, which include not only the monthly component series but also such an important quarterly series as the real GDP. The evaluation of six different nonlinear models suggests that the probabilities derived from all the models comply with NBER business cycle dating and detect a one-time shifting from high variance to low-variance states in February 1984. In addition, we find that: mixed-frequency models outperform single-frequency models; restricted models outperform unrestricted models; four-regime switching models outperform two-regime switching models.