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A mixed frequency BVAR for the euro area labour market

Author

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  • Consolo, Agostino
  • Foroni, Claudia
  • Martínez Hernández, Catalina

Abstract

We introduce a Bayesian Mixed-Frequency VAR model for the aggregate euro area labour market that features a structural identification via sign restrictions. The purpose of this paper is twofold: we aim at (i) providing reliable and timely forecasts of key labour market variables and (ii) enhancing the economic interpretation of the main movements in the labour market. We find satisfactory results in terms of forecasting, especially when looking at quarterly variables, such as employment growth and the job finding rate. Furthermore, we look into the shocks that drove the labour market and macroeconomic dynamics from 2002 to early 2020, with a first insight also on the COVID-19 recession. While domestic and foreign demand shocks were the main drivers during the Global Financial Crisis, aggregate supply conditions and labour supply factors reflecting the degree of lockdown-related restrictions have been important drivers of key labour market variables during the pandemic. JEL Classification: J6, C53, C32, C11

Suggested Citation

  • Consolo, Agostino & Foroni, Claudia & Martínez Hernández, Catalina, 2021. "A mixed frequency BVAR for the euro area labour market," Working Paper Series 2601, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20212601
    Note: 3572376
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    References listed on IDEAS

    as
    1. Koenig, Felix & Manning, Alan & Petrongolo, Barbara, 2014. "Reservation wages and the wage flexibility puzzle," LSE Research Online Documents on Economics 60613, London School of Economics and Political Science, LSE Library.
    2. D’Amuri, Francesco & Marcucci, Juri, 2017. "The predictive power of Google searches in forecasting US unemployment," International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
    3. Geweke, John & Amisano, Gianni, 2010. "Comparing and evaluating Bayesian predictive distributions of asset returns," International Journal of Forecasting, Elsevier, vol. 26(2), pages 216-230, April.
    4. Kuzin, Vladimir & Marcellino, Massimiliano & Schumacher, Christian, 2011. "MIDAS vs. mixed-frequency VAR: Nowcasting GDP in the euro area," International Journal of Forecasting, Elsevier, vol. 27(2), pages 529-542.
    5. Jordi Galí & Frank Smets & Rafael Wouters, 2012. "Unemployment in an Estimated New Keynesian Model," NBER Macroeconomics Annual, University of Chicago Press, vol. 26(1), pages 329-360.
    6. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2020. "Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 934-943, September.
    7. Nikolaos Askitas & Klaus F. Zimmermann, 2009. "Google Econometrics and Unemployment Forecasting," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 55(2), pages 107-120.
    8. Foroni, Claudia & Guérin, Pierre & Marcellino, Massimiliano, 2018. "Using low frequency information for predicting high frequency variables," International Journal of Forecasting, Elsevier, vol. 34(4), pages 774-787.
    9. Mark Gertler & Luca Sala & Antonella Trigari, 2008. "An Estimated Monetary DSGE Model with Unemployment and Staggered Nominal Wage Bargaining," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 40(8), pages 1713-1764, December.
    10. repec:zbw:bofrdp:2018_014 is not listed on IDEAS
    11. Anderton, Robert & Botelho, Vasco & Consolo, Agostino & Da Silva, António Dias & Foroni, Claudia & Mohr, Matthias & Vivian, Lara, 2021. "The impact of the COVID-19 pandemic on the euro area labour market," Economic Bulletin Articles, European Central Bank, vol. 8.
    12. Michael W. L. Elsby & Bart Hobijn & Ayşegül Şahin, 2013. "Unemployment Dynamics in the OECD," The Review of Economics and Statistics, MIT Press, vol. 95(2), pages 530-548, May.
    13. Consolo, Agostino & Da Silva, António Dias, 2019. "The euro area labour market through the lens of the Beveridge curve," Economic Bulletin Articles, European Central Bank, vol. 4.
    14. 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.
    15. Baumeister, Christiane & Hamilton, James D., 2018. "Inference in structural vector autoregressions when the identifying assumptions are not fully believed: Re-evaluating the role of monetary policy in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 48-65.
    16. Sims, Christopher A & Zha, Tao, 1998. "Bayesian Methods for Dynamic Multivariate Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 949-968, November.
    17. Christopher A. Pissarides, 2000. "Equilibrium Unemployment Theory, 2nd Edition," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262161877, April.
    18. Phaneuf, Louis & Sims, Eric & Victor, Jean Gardy, 2018. "Inflation, output and markup dynamics with purely forward-looking wage and price setters," European Economic Review, Elsevier, vol. 105(C), pages 115-134.
    19. Jordi Galí & Mark Gertler & J. David López-Salido, 2007. "Markups, Gaps, and the Welfare Costs of Business Fluctuations," The Review of Economics and Statistics, MIT Press, vol. 89(1), pages 44-59, November.
    20. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    21. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    22. Regis Barnichon & Christopher J. Nekarda, 2012. "The Ins and Outs of Forecasting Unemployment: Using Labor Force Flows to Forecast the Labor Market," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(2 (Fall)), pages 83-131.
    23. Koenig, Felix & Manning, Alan & Petrongolo, Barbara, 2014. "Reservation wages and the wage flexibility puzzle," LSE Research Online Documents on Economics 60613, London School of Economics and Political Science, LSE Library.
    24. Lawrence J. Christiano & Martin S. Eichenbaum & Mathias Trabandt, 2016. "Unemployment and Business Cycles," Econometrica, Econometric Society, vol. 84(4), pages 1523-1569, July.
    25. Baumeister, Christiane & Hamilton, James, 2018. "Inference in Structural Vector Autoregressions When the Identifying Assumptions are Not Fully Believed: Re-evaluating the Role," CEPR Discussion Papers 12911, C.E.P.R. Discussion Papers.
    26. Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Adaptive hierarchical priors for high-dimensional vector autoregressions," Journal of Econometrics, Elsevier, vol. 212(1), pages 241-271.
    27. Frank Schorfheide & Dongho Song, 2015. "Real-Time Forecasting With a Mixed-Frequency VAR," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 366-380, July.
    28. Ghysels, Eric, 2016. "Macroeconomics and the reality of mixed frequency data," Journal of Econometrics, Elsevier, vol. 193(2), pages 294-314.
    29. Haroon Mumtaz & Francesco Zanetti, 2015. "Labor Market Dynamics: A Time-Varying Analysis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(3), pages 319-338, June.
    30. Barnichon, Regis & Garda, Paula, 2016. "Forecasting unemployment across countries: The ins and outs," European Economic Review, Elsevier, vol. 84(C), pages 165-183.
    31. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    32. Brave, Scott A. & Butters, R. Andrew & Justiniano, Alejandro, 2019. "Forecasting economic activity with mixed frequency BVARs," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1692-1707.
    33. Claudia Foroni & Massimiliano Marcellino, 2016. "Mixed frequency structural vector auto-regressive models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(2), pages 403-425, February.
    34. Claudia Foroni & Francesco Furlanetto & Antoine Lepetit, 2018. "Labor Supply Factors And Economic Fluctuations," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(3), pages 1491-1510, August.
    35. Kadiyala, K Rao & Karlsson, Sune, 1997. "Numerical Methods for Estimation and Inference in Bayesian VAR-Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(2), pages 99-132, March-Apr.
    36. Haroon Mumtaz & Francesco Zanetti, 2012. "Neutral Technology Shocks And The Dynamics Of Labor Input: Results From An Agnostic Identification," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(1), pages 235-254, February.
    37. Litterman, Robert, 1986. "Forecasting with Bayesian vector autoregressions -- Five years of experience : Robert B. Litterman, Journal of Business and Economic Statistics 4 (1986) 25-38," International Journal of Forecasting, Elsevier, vol. 2(4), pages 497-498.
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    Cited by:

    1. Consolo, Agostino & Petroulakis, Filippos, 2022. "Did COVID-19 induce a reallocation wave?," Working Paper Series 2703, European Central Bank.
    2. Blagov, Boris & Schmidt, Torsten C., 2022. "Schätzung der Wirtschaftsentwicklung in NRW im dritten Quartal 2022: Ein Mixed-Frequency-Ansatz," RWI Konjunkturberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, vol. 73(4), pages 53-59.

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    More about this item

    Keywords

    Bayesian VAR; labour market; mixed frequency data;
    All these keywords.

    JEL classification:

    • J6 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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