We develop a formal statistical approach to investigate the possibility that leading indicator variables have different lead times at business cycle peaks and troughs. For this purpose, we propose a novel Markov switching vector autoregressive model, where economic growth and leading indicators share a common Markov process determining the state, but such that their cycles are non-synchronous with the non-synchronicity varying across the different regimes. An empirical application to monthly US industrial production (IP) and The Conference Board's Composite Index of Leading Indicators (CLI) for the period 1959-2004 shows that on average the CLI leads IP by more than seven months at peaks, but only by three and a half months at troughs. In terms of timeliness, the CLI is therefore most useful for signalling oncoming recessions. Furthermore, we find that allowing for asymmetric lead times leads to improved real-time dating of business cycle peaks and troughs and more accurate forecasts of turning points and IP growth.

Additional Metadata
Keywords Bayesian inference, Markov switching, business cycles, leading indicators, real-time data
JEL Bayesian Analysis (jel C11), Time-Series Models; Dynamic Quantile Regressions (jel C32), Model Construction and Estimation (jel C51), Business Fluctuations; Cycles (jel E32)
Persistent URL hdl.handle.net/1765/9230
Series Econometric Institute Research Papers
Journal Report / Econometric Institute, Erasmus University Rotterdam
Paap, R, Segers, R, & van Dijk, D.J.C. (2007). Do leading indicators lead peaks more than troughs? (No. EI 2007-08). Report / Econometric Institute, Erasmus University Rotterdam. Retrieved from http://hdl.handle.net/1765/9230