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A new approach to dating the reference cycle

Author

Listed:
  • Máximo Camacho

    () (University of Murcia)

  • María Dolores Gadea

    () (University of Zaragoza)

  • Ana Gómez Loscos

    () (Banco de España)

Abstract

This paper proposes a new approach to the analysis of the reference cycle turning points, defined on the basis of the specific turning points of a broad set of coincident economic indicators. Each individual pair of specific peaks and troughs from these indicators is viewed as a realization of a mixture of an unspecified number of separate bivariate Gaussian distributions whose different means are the reference turning points. These dates break the sample into separate reference cycle phases, whose shifts are modeled by a hidden Markov chain. The transition probability matrix is constrained so that the specification is equivalent to a multiple changepoint model. Bayesian estimation of finite Markov mixture modeling techniques is suggested to estimate the model. Several Monte Carlo experiments are used to show the accuracy of the model to date reference cycles that suffer from short phases, uncertain turning points, small samples and asymmetric cycles. In the empirical section, we show the high performance of our approach to identifying the US reference cycle, with little difference from the timing of the turning point dates established by the NBER. In a pseudo real-time analysis, we also show the good performance of this methodology in terms of accuracy and speed of detection of turning point dates.

Suggested Citation

  • Máximo Camacho & María Dolores Gadea & Ana Gómez Loscos, 2019. "A new approach to dating the reference cycle," Working Papers 1914, Banco de España;Working Papers Homepage.
  • Handle: RePEc:bde:wpaper:1914
    as

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    File URL: https://www.bde.es/f/webbde/SES/Secciones/Publicaciones/PublicacionesSeriadas/DocumentosTrabajo/19/Fich/dt1914e.pdf
    File Function: First version, May 2019
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    References listed on IDEAS

    as
    1. Harding, Don & Pagan, Adrian, 2006. "Synchronization of cycles," Journal of Econometrics, Elsevier, vol. 132(1), pages 59-79, May.
    2. Don Harding & Adrian Pagan, 2016. "The Econometric Analysis of Recurrent Events in Macroeconomics and Finance," Economics Books, Princeton University Press, edition 1, number 10744.
    3. Chan, Joshua C.C. & Koop, Gary, 2014. "Modelling breaks and clusters in the steady states of macroeconomic variables," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 186-193.
    4. Stock, James H. & Watson, Mark W., 2014. "Estimating turning points using large data sets," Journal of Econometrics, Elsevier, vol. 178(P2), pages 368-381.
    5. repec:hrv:faseco:33192198 is not listed on IDEAS
    6. Watson, Mark W, 1994. "Business-Cycle Durations and Postwar Stabilization of the U.S. Economy," American Economic Review, American Economic Association, vol. 84(1), pages 24-46, March.
    7. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
    8. Arthur F. Burns & Wesley C. Mitchell, 1946. "Measuring Business Cycles," NBER Books, National Bureau of Economic Research, Inc, number burn46-1.
    9. Giusto, Andrea & Piger, Jeremy, 2017. "Identifying business cycle turning points in real time with vector quantization," International Journal of Forecasting, Elsevier, vol. 33(1), pages 174-184.
    10. Gerhard Bry & Charlotte Boschan, 1971. "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs," NBER Books, National Bureau of Economic Research, Inc, number bry_71-1.
    11. Chauvet, Marcelle & Piger, Jeremy, 2008. "A Comparison of the Real-Time Performance of Business Cycle Dating Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 42-49, January.
    12. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 66-77, January.
    13. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
    14. Gerhard Bry & Charlotte Boschan, 1971. "Foreword to "Cyclical Analysis of Time Series: Selected Procedures and Computer Programs"," NBER Chapters, in: Cyclical Analysis of Time Series: Selected Procedures and Computer Programs, pages -1, National Bureau of Economic Research, Inc.
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    16. Ko, Stanley I. M. & Chong, Terence T. L. & Ghosh, Pulak, 2014. "Dirichlet Process Hidden Markov Multiple Change-point Model," MPRA Paper 57871, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    business cycles; turning points; finite mixture models;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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