Do Leading Indicators Lead Peaks More Than Troughs?
- 1 October 2009
- journal article
- Published by Informa UK Limited in Journal of Business & Economic Statistics
- Vol. 27 (4), 528-543
- https://doi.org/10.1198/jbes.2009.07061
Abstract
We develop a novel Markov switching vector autoregressive model to investigate the possibility that leading indicators have different lead times at business cycle peaks and at troughs. In this model, coincident and leading indicators share a common Markov state process, but their cycles are nonsynchronous, with the nonsynchronicity varying across regimes. An application shows that on average the Conference Board’s Composite Leading Index leads the Composite Coincident Index by nearly 1 year at peaks but by only 1 quarter at troughs. Allowing for asymmetric lead times yields improved real-time dating and forecasting of business cycle turning points.Keywords
This publication has 30 references indexed in Scilit:
- A Comparison of the Real-Time Performance of Business Cycle Dating MethodsJournal of Business & Economic Statistics, 2008
- Business Cycle AsymmetriesJournal of Business & Economic Statistics, 2003
- This is what the leading indicators leadJournal of Applied Econometrics, 2002
- An Econometric Characterization of Business Cycle Dynamics with Factor Structure and Regime SwitchingInternational Economic Review, 1998
- Calculating posterior distributions and modal estimates in Markov mixture modelsJournal of Econometrics, 1996
- Measuring Business Cycles: A Modern PerspectiveThe Review of Economics and Statistics, 1996
- A Check on the Robustness of Hamilton's Markov Switching Model Approach to the Economic Analysis of the Business CycleStudies in Nonlinear Dynamics and Econometrics, 1996
- Comparing Predictive AccuracyJournal of Business & Economic Statistics, 1995
- Bayes Inference via Gibbs Sampling of Autoregressive Time Series Subject to Markov Mean and Variance ShiftsJournal of Business & Economic Statistics, 1993
- Forecasting Output With the Composite Leading Index: A Real-Time AnalysisJournal of the American Statistical Association, 1991