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Do Leading Indicators Lead Peaks More Than Troughs?

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  • Paap, Richard
  • Segers, Rene
  • van Dijk, Dick

Abstract

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.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 27 (2009)
Issue (Month): 4 ()
Pages: 528-543

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Handle: RePEc:bes:jnlbes:v:27:i:4:y:2009:p:528-543

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  1. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, December.
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Citations

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Cited by:
  1. Jonas Dovern & Christina Ziegler, 2008. "Predicting Growth Rates and Recessions. Assessing U.S. Leading Indicators Under Real-Time Conditions," Kiel Working Papers 1397, Kiel Institute for the World Economy.
  2. Cem Cakmakli & Richard Paap & Dick van Dijk, 2011. "Measuring and Predicting Heterogeneous Recessions," Tinbergen Institute Discussion Papers 11-154/4, Tinbergen Institute, revised 15 Nov 2011.
  3. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
  4. Rubén Hernández-Murillo & Michael T. Owyang & Margarita Rubio, 2013. "Clustered housing cycles," Working Papers 2013-021, Federal Reserve Bank of St. Louis.
    • Rubén Hernández-Murillo & Michael T Owyang & Margarita Rubio, 2013. "Clustered Housing Cycles," Discussion Papers 2013/02, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
  5. Makram El-Shagi & Gregor von Schweinitz, 2012. "Qual VAR Revisited: Good Forecast, Bad Story," IWH Discussion Papers 12, Halle Institute for Economic Research.
  6. Shaun P Vahey & Elizabeth C Wakerly, 2013. "Moving towards probability forecasting," BIS Papers chapters, in: Bank for International Settlements (ed.), Globalisation and inflation dynamics in Asia and the Pacific, volume 70, pages 3-8 Bank for International Settlements.
  7. Cem Cakmakli & Richard Paap & Dick van Dijk, 2011. "Measuring and Predicting Heterogeneous Recessions," Tinbergen Institute Discussion Papers 11-154/4, Tinbergen Institute, revised 15 Nov 2011.
  8. Camacho, Maximo, 2013. "Mixed-frequency VAR models with Markov-switching dynamics," Economics Letters, Elsevier, vol. 121(3), pages 369-373.
  9. Sergey Smirnov, 2011. "Those Unpredictable Recessions," HSE Working papers WP BRP 02/EC/2011, National Research University Higher School of Economics.

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