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Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic

Author

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  • Morley, James
  • Rodríguez-Palenzuela, Diego
  • Sun, Yiqiao
  • Wong, Benjamin
Abstract
We estimate the euro area output gap by applying the Beveridge–Nelson decomposition based on a large Bayesian vector autoregression. Our approach incorporates multivariate information through the inclusion of a wide range of variables in the analysis and addresses data issues associated with the COVID-19 pandemic. The estimated output gap lines up well with the CEPR chronology of the business cycle for the euro area and we find that hours worked, more than the unemployment rate, provides the key source of information about labor utilization in the economy, especially in pinning down the depth of the output gap during the COVID-19 recession when the unemployment rate rose only moderately. Our findings confirm that labor market adjustments to the business cycle in the euro area occur more through the intensive, rather than extensive, margin.

Suggested Citation

  • Morley, James & Rodríguez-Palenzuela, Diego & Sun, Yiqiao & Wong, Benjamin, 2023. "Estimating the euro area output gap using multivariate information and addressing the COVID-19 pandemic," European Economic Review, Elsevier, vol. 153(C).
    • Handle: RePEc:eee:eecrev:v:153:y:2023:i:c:s0014292123000144
    DOI: 10.1016/j.euroecorev.2023.104385
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    1. Gary M. Koop, 2013. "Forecasting with Medium and Large Bayesian VARS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 177-203, March.
    2. Marcellino, Massimiliano & Musso, Alberto, 2011. "The reliability of real-time estimates of the euro area output gap," Economic Modelling, Elsevier, vol. 28(4), pages 1842-1856, July.
    3. Athanasios Orphanides & Simon van Norden, 2002. "The Unreliability of Output-Gap Estimates in Real Time," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 569-583, November.
    4. James Morley & Benjamin Wong, 2020. "Estimating and accounting for the output gap with large Bayesian vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(1), pages 1-18, January.
    5. Michele Lenza & Giorgio E. Primiceri, 2022. "How to estimate a vector autoregression after March 2020," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 688-699, June.
    6. Evans, George & Reichlin, Lucrezia, 1994. "Information, forecasts, and measurement of the business cycle," Journal of Monetary Economics, Elsevier, vol. 33(2), pages 233-254, April.
    7. Günes Kamber & James Morley & Benjamin Wong, 2018. "Intuitive and Reliable Estimates of the Output Gap from a Beveridge-Nelson Filter," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 550-566, July.
    8. Kamber, Güneş & Wong, Benjamin, 2020. "Global factors and trend inflation," Journal of International Economics, Elsevier, vol. 122(C).
    9. Tino Berger & Christian Ochsner, 2022. "Tracking the German Business Cycle," MAGKS Papers on Economics 202212, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    10. Marek Jarociński & Michele Lenza, 2018. "An Inflation‐Predicting Measure of the Output Gap in the Euro Area," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(6), pages 1189-1224, September.
    11. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    12. Michael W. McCracken & Serena Ng, 2021. "FRED-QD: A Quarterly Database for Macroeconomic Research," Review, Federal Reserve Bank of St. Louis, vol. 103(1), pages 1-44, January.
    13. Marek Jarociński & Bartosz Maćkowiak, 2017. "Granger Causal Priority and Choice of Variables in Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 319-329, May.
    14. Ohanian, Lee E. & Raffo, Andrea, 2012. "Aggregate hours worked in OECD countries: New measurement and implications for business cycles," Journal of Monetary Economics, Elsevier, vol. 59(1), pages 40-56.
    15. González-Astudillo, Manuel, 2019. "An output gap measure for the euro area: Exploiting country-level and cross-sectional data heterogeneity," European Economic Review, Elsevier, vol. 120(C).
    16. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2021. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," Working Papers 21-02R, Federal Reserve Bank of Cleveland, revised 09 Aug 2021.
    17. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino & Elmar Mertens, 2024. "Addressing COVID-19 Outliers in BVARs with Stochastic Volatility," The Review of Economics and Statistics, MIT Press, vol. 106(5), pages 1403-1417, September.
    18. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    19. Simon Gilchrist & Benoit Mojon, 2018. "Credit Risk in the Euro Area," Economic Journal, Royal Economic Society, vol. 128(608), pages 118-158, February.
    20. Berger, Tino & Richter, Julia & Wong, Benjamin, 2022. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    21. Justiniano, Alejandro & Preston, Bruce, 2010. "Can structural small open-economy models account for the influence of foreign disturbances?," Journal of International Economics, Elsevier, vol. 81(1), pages 61-74, May.
    22. Burda, Michael C. & Hunt, Jennifer, 2011. "What explains the German labor market miracle in the Great Recession?," SFB 649 Discussion Papers 2011-031, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    23. Camba-Mendez, Gonzalo & Rodriguez-Palenzuela, Diego, 2003. "Assessment criteria for output gap estimates," Economic Modelling, Elsevier, vol. 20(3), pages 529-562, May.
    24. Simon Gilchrist & Benoit Mojon, 2018. "Credit Risk in the Euro Area," Economic Journal, Royal Economic Society, vol. 128(608), pages 118-158, February.
    25. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    26. Berger, Tino & Morley, James & Wong, Benjamin, 2023. "Nowcasting the output gap," Journal of Econometrics, Elsevier, vol. 232(1), pages 18-34.
      • Tino Berger & James Morley & Benjamin Wong, 2020. "Nowcasting the output gap," CAMA Working Papers 2020-78, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    27. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    28. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    29. Beveridge, Stephen & Nelson, Charles R., 1981. "A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the `business cycle'," Journal of Monetary Economics, Elsevier, vol. 7(2), pages 151-174.
    30. Alessandro Barbarino & Travis J. Berge & Han Chen & Andrea Stella, 2020. "Which Output Gap Estimates Are Stable in Real Time and Why?," Finance and Economics Discussion Series 2020-102, Board of Governors of the Federal Reserve System (U.S.).
    31. James C. Morley & Charles R. Nelson & Eric Zivot, 2003. "Why Are the Beveridge-Nelson and Unobserved-Components Decompositions of GDP So Different?," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 235-243, May.
    32. Morley, James C., 2002. "A state-space approach to calculating the Beveridge-Nelson decomposition," Economics Letters, Elsevier, vol. 75(1), pages 123-127, March.
    33. Michael C. Burda & Jennifer Hunt, 2011. "What Explains the German Labor Market Miracle in the Great Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 42(1 (Spring), pages 273-335.
    34. Berger, Tino & Richter, Julia & Wong, Benjamin, 2022. "A unified approach for jointly estimating the business and financial cycle, and the role of financial factors," Journal of Economic Dynamics and Control, Elsevier, vol. 136(C).
    35. Fabio Busetti & Michele Caivano, 2016. "The trend–cycle decomposition of output and the Phillips curve: Bayesian estimates for Italy and the Euro area," Empirical Economics, Springer, vol. 50(4), pages 1565-1587, June.
    36. Charles A. Fleischman & John M. Roberts, 2011. "From many series, one cycle: improved estimates of the business cycle from a multivariate unobserved components model," Finance and Economics Discussion Series 2011-46, Board of Governors of the Federal Reserve System (U.S.).
    37. Michele Lenza & Giorgio E. Primiceri, 2020. "How to Estimate a VAR after March 2020," NBER Working Papers 27771, National Bureau of Economic Research, Inc.
    38. Bobeica, Elena & Hartwig, Benny, 2021. "The COVID-19 shock and challenges for time series models," Working Paper Series 2558, European Central Bank.
    39. Antonio M. Conti & Elisa Guglielminetti & Marianna Riggi, 2019. "Labour productivity and the wageless recovery," Temi di discussione (Economic working papers) 1257, Bank of Italy, Economic Research and International Relations Area.
    40. Canova, Fabio, 2020. "FAQ: How do I extract the output gap?," Working Paper Series 386, Sveriges Riksbank (Central Bank of Sweden).
    41. Zha, Tao, 1999. "Block recursion and structural vector autoregressions," Journal of Econometrics, Elsevier, vol. 90(2), pages 291-316, June.
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    2. Tino Berger & Lorenzo Pozzi, 2023. "Cyclical consumption," Tinbergen Institute Discussion Papers 23-064/VI, Tinbergen Institute.
    3. Granados, Camilo & Parra-Amado, Daniel, 2024. "Estimating the output gap after COVID: How to address unprecedented macroeconomic variations," Economic Modelling, Elsevier, vol. 135(C).

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    More about this item

    Keywords

    Beveridge–Nelson decomposition; Output gap; Multivariate information;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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