Working Paper: CEPR ID: DP8866
Authors: Maximo Camacho; Gabriel Pérez-Quiros; Pilar Poncela
Abstract: We extend the Markov-switching dynamic factor model to account for some of the specificities of the day-to-day monitoring of economic developments from macroeconomic indicators, such as ragged edges and mixed frequencies. We examine the theoretical benefits of this extension and corroborate the results through several MonteCarlo simulations. Finally, we assess its empirical reliability to compute real-time inferences of the US business cycle.
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 |
|---|---|
| adapting the traditional MSDFM to handle ragged ends and mixed frequencies (C22) | improves the inference about the business cycle (E32) |
| using incoming information from promptly published economic indicators (E37) | enhances the accuracy of business cycle assessments (E32) |
| performance gains from incorporating monthly indicators alongside quarterly indicators (C43) | diminish when the quality of existing monthly indicators is high or when additional quarterly indicators are noisy (C32) |
| model extension (C59) | outperforms traditional MSDFM methods (C52) |
| Monte Carlo simulations (C15) | support claims about improvements in real-time inferences (C53) |