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Output and Inflation Co-movement: An Update on Business-Cycle Stylized Facts

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  • Michal Andrle
  • Jan Bruha
  • Mr. Serhat Solmaz

Abstract

What are the drivers of business cycle fluctuations? And how many are there? By documenting strong and predictable co-movement of real variables during the business cycle in a sample of advanced economies, we argue that most business cycle fluctuations are driven by one major factor. The positive co-movement of real output and inflation convincingly argues for a demand story. We propose a simple statistic that can compare data and models. Based on this statistic, we show that the recent vintage of structural economic models has difficulties replicating the stylized facts we document.

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  • Michal Andrle & Jan Bruha & Mr. Serhat Solmaz, 2016. "Output and Inflation Co-movement: An Update on Business-Cycle Stylized Facts," IMF Working Papers 2016/241, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2016/241
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    1. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    2. Finn E. Kydland & Edward C. Prescott, 1990. "Business cycles: real facts and a monetary myth," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 14(Spr), pages 3-18.
    3. Michal Andrle & Jan Bruha & Serhat Solmaz, 2016. "On the Sources of Business Cycles: Implications for DSGE Models," Working Papers 2016/03, Czech National Bank.
    4. Brent Meyer & Saeed Zaman, 2013. "It’s not just for inflation: The usefulness of the median CPI in BVAR forecasting," Working Papers (Old Series) 1303, Federal Reserve Bank of Cleveland.
    5. Charles P. Kindleberger & Robert Z. Aliber, 2005. "Manias, Panics and Crashes," Palgrave Macmillan Books, Palgrave Macmillan, edition 0, number 978-0-230-62804-5.
    6. Pollock D. S. G., 2013. "Cycles, Syllogisms and Semantics: Examining the Idea of Spurious Cycles," Journal of Time Series Econometrics, De Gruyter, vol. 6(1), pages 81-102, September.
    7. Lawrence H. Summers, 1986. "Some skeptical observations on real business cycle theory," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 10(Fall), pages 23-27.
    8. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-162, April.
    9. Michal Andrle & Jan Bruha & Serhat Solmaz, 2013. "Inflation and Output Comovement in the Euro Area: Love at Second Sight?," Working Papers 2013/07, Czech National Bank.
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    Cited by:

    1. Eugenio Cerutti & Stijn Claessens & Andrew K. Rose, 2019. "How Important is the Global Financial Cycle? Evidence from Capital Flows," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 67(1), pages 24-60, March.
    2. Jan Bruha & Jaromir Tonner, 2018. "An Exchange Rate Floor as an Instrument of Monetary Policy: An Ex-Post Assessment of the Czech Experience," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 68(6), pages 537-549, December.
    3. repec:cnb:ocpubc:geo2021/11 is not listed on IDEAS
    4. Ľubica Štiblárová, 2020. "Traditional and Alternative Business Cycle Dating Procedures: Evidence for the Euro Area and the Czech Economy," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 68(1), pages 263-274.
    5. Oxana Babecka Kucharcukova & Jan Bruha & Petr Kral & Martin Motl & Jaromir Tonner, 2022. "Assessment of the Nature of the Pandemic Shock: Implications for Monetary Policy," Research and Policy Notes 2022/01, Czech National Bank.

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