Extracting Nonlinear Signals from Several Economic Indicators
研究了如何用马尔可夫转换动态因子模型从多个经济指标中识别商业周期转折点，比较了一步法和两步法的效果，并分析了增加指标数量的作用。
We develop a twofold analysis of how the information provided by several economic indicators can be used in Markov switching dynamic factor models to identify the business cycle turning points. First, we compare the performance of a fully nonlinear multivariate specification (one-step approach) with the 'shortcut' of using a linear factor model to obtain a coincident indicator, which is then used to compute the Markov switching probabilities (two-step approach). Second, we examine the role of increasing the number of indicators. Our results suggest that one step is generally preferred to two steps, especially in the vicinity of turning points, although its gains diminish as the quality of the indicators increases. Additionally, we also obtain decreasing returns of adding more indicators with similar signal-to-noise ratios. Using the four constituent series of the Stock–Watson coincident index, we illustrate these results for US data. Copyright © 2014 John Wiley & Sons, Ltd.