Working Paper: CEPR ID: DP8865
Authors: Maximo Camacho; Gabriel Pérez-Quiros; Pilar Poncela
Abstract: 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 non- linear 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, although its marginal gains diminish as the quality of the indicators increases and as more indicators are used to identify the non-linear signal. Using the four constituent series of the Stock-Watson coincident index, we illustrate these results for US data.
Keywords: Business Cycles; Output Growth; Time Series
JEL Codes: C22; E27; E32
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| one-step approach (C20) | preferred over two-step approach (C20) |
| one-step approach (C20) | generally outperforms two-step method (C52) |
| carefully selected economic indicators (E30) | one-step method's performance does not significantly exceed two-step method (C20) |
| inclusion of additional indicators (C43) | enhance precision of business cycle inference (E32) |
| number of indicators increases (C43) | incremental benefits decrease (D61) |
| one-step method (C20) | reduction in mean square errors in identifying first month after phase shift (C32) |
| one-step procedure (C20) | superior performance in real-time applications (C69) |