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Monitoring the world business cycle

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  • Maximo Camacho
  • Jaime Martinez-Martin

Abstract

We propose a Markov-switching dynamic factor model to construct an index of global business cycle conditions, to perform short-term forecasts of world GDP quarterly growth in real time and to compute real-time business cycle probabilities. To overcome the real-time forecasting challenges, the model accounts for mixed frequencies, for asynchronous data publication and for leading indicators. Our pseudo real-time results show that this approach provides reliable and timely inferences of the world quarterly growth and of the world state of the business cycle on a monthly basis.

Suggested Citation

  • Maximo Camacho & Jaime Martinez-Martin, 2015. "Monitoring the world business cycle," Globalization Institute Working Papers 228, Federal Reserve Bank of Dallas.
  • Handle: RePEc:fip:feddgw:228
    DOI: 10.24149/gwp228
    Note: Published as: Camacho, Maximo and Jaime Martinez-Martin (2015), "Monitoring the World Business Cycle," Economic Modeling 51: 617-625.
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    Cited by:

    1. Carstensen, Kai & Heinrich, Markus & Reif, Magnus & Wolters, Maik H., 2020. "Predicting ordinary and severe recessions with a three-state Markov-switching dynamic factor model," International Journal of Forecasting, Elsevier, vol. 36(3), pages 829-850.
    2. Kose, M. Ayhan & Sugawara, Naotaka & Terrones, Marco E., 2020. "Global Recessions," MPRA Paper 98608, University Library of Munich, Germany.
    3. Libero Monteforte & Valentina Raponi, 2019. "Short‐term forecasts of economic activity: Are fortnightly factors useful?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(3), pages 207-221, April.
    4. Magnus Reif, 2020. "Macroeconomics, Nonlinearities, and the Business Cycle," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 87.
    5. Camacho, Maximo & Martinez-Martin, Jaime, 2015. "Monitoring the world business cycle," Economic Modelling, Elsevier, vol. 51(C), pages 617-625.
    6. Baumann, Ursel & Gómez-Salvador, Ramón & Seitz, Franz, 2019. "Detecting turning points in global economic activity," Working Paper Series 2310, European Central Bank.
    7. Funashima, Yoshito, 2016. "Governmentally amplified output volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 469-478.

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    More about this item

    JEL classification:

    • 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
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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