IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v35y2019i4p1613-1626.html
   My bibliography  Save this article

Asymmetry in unemployment rate forecast errors

Author

Listed:
  • Galbraith, John W.
  • van Norden, Simon

Abstract

Asymmetries in unemployment dynamics have been observed in the time series of a number of countries, including the United States. This paper studies asymmetries in unemployment rate forecast errors. We consider conditions under which optimal forecasts will display asymmetrically-distributed errors and how the degree of asymmetry might vary with the forecast horizon. Using data from the U.S. Survey of Professional Forecasters and the Federal Reserve Greenbook, we find substantial evidence of forecast error asymmetry, which tends to increase with the forecast horizon; we also find noteworthy differences in forecasts from these two sources. The results give insight into the abilities of professional forecasters to adapt their forecasts to asymmetry in underlying processes.

Suggested Citation

  • Galbraith, John W. & van Norden, Simon, 2019. "Asymmetry in unemployment rate forecast errors," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1613-1626.
    • Handle: RePEc:eee:intfor:v:35:y:2019:i:4:p:1613-1626
    DOI: 10.1016/j.ijforecast.2018.11.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207018302000
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijforecast.2018.11.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Koop, Gary & Potter, Simon M, 1999. "Dynamic Asymmetries in U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 298-312, July.
    2. Croushore, Dean & van Norden, Simon, 2019. "Fiscal Surprises at the FOMC," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1583-1595.
    3. Michael S. Smith & Shaun P. Vahey, 2016. "Asymmetric Forecast Densities for U.S. Macroeconomic Variables from a Gaussian Copula Model of Cross-Sectional and Serial Dependence," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 416-434, July.
    4. John W. Galbraith & Simon van Norden, 2012. "Assessing gross domestic product and inflation probability forecasts derived from Bank of England fan charts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(3), pages 713-727, July.
    5. Sichel, Daniel E, 1989. "Are Business Cycles Asymmetric? A Correction," Journal of Political Economy, University of Chicago Press, vol. 97(5), pages 1255-1260, October.
    6. Dean Croushore & Simon van Norden, 2018. "Fiscal Forecasts at the FOMC: Evidence from the Greenbooks," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 933-945, December.
    7. Jushan Bai & Serena Ng, 2005. "Tests for Skewness, Kurtosis, and Normality for Time Series Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 49-60, January.
    8. Falk, Barry, 1986. "Further Evidence on the Asymmetric Behavior of Economic Time Series over the Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1096-1109, October.
    9. David Reifschneider & Peter Tulip, 2017. "Gauging the Uncertainty of the Economic Outlook Using Historical Forecasting Errors: The Federal Reserve's Approach," RBA Research Discussion Papers rdp2017-01, Reserve Bank of Australia.
    10. Dean Croushore, 1993. "Introducing: the survey of professional forecasters," Business Review, Federal Reserve Bank of Philadelphia, issue Nov, pages 3-15.
    11. Malte Knüppel & Guido Schultefrankenfeld, 2012. "How Informative Are Central Bank Assessments of Macroeconomic Risks?," International Journal of Central Banking, International Journal of Central Banking, vol. 8(3), pages 87-139, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrea Carriero & Todd E. Clark & Marcellino Massimiliano, 2020. "Nowcasting Tail Risks to Economic Activity with Many Indicators," Working Papers 20-13R2, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
    2. Pfarrhofer, Michael, 2022. "Modeling tail risks of inflation using unobserved component quantile regressions," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    3. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2024. "Capturing Macro‐Economic Tail Risks with Bayesian Vector Autoregressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(5), pages 1099-1127, August.
    4. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2024. "Investigating Growth-at-Risk Using a Multicountry Nonparametric Quantile Factor Model," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 42(4), pages 1302-1317, October.
    5. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    6. Donayre, Luiggi, 2022. "On the behavior of Okun's law across business cycles," Economic Modelling, Elsevier, vol. 112(C).
    7. Tamás Kiss & Stepan Mazur & Hoang Nguyen & Pär Österholm, 2023. "Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 347-368, March.
    8. Tanujit Chakraborty & Ashis Kumar Chakraborty & Munmun Biswas & Sayak Banerjee & Shramana Bhattacharya, 2021. "Unemployment Rate Forecasting: A Hybrid Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 183-201, January.
    9. Bas Scheer, 2022. "Addressing Unemployment Rate Forecast Errors in Relation to the Business Cycle," CPB Discussion Paper 434, CPB Netherlands Bureau for Economic Policy Analysis.
    10. Ovidiu BUZOIANU & Amelia DIACONU & Marcela Antoaneta NICULESCU & Silviu DIACONU, 2020. "Analysis Of The Economic And Social Effects Of Unemployment In Romania," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 705-711, November.
    11. Muneeb Ahmad & Yousaf Ali Khan & Chonghui Jiang & Syed Jawad Haider Kazmi & Syed Zaheer Abbas, 2023. "The impact of COVID‐19 on unemployment rate: An intelligent based unemployment rate prediction in selected countries of Europe," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 528-543, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. McKay, Alisdair & Reis, Ricardo, 2008. "The brevity and violence of contractions and expansions," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 738-751, May.
    2. Clements, Michael P., 2018. "Are macroeconomic density forecasts informative?," International Journal of Forecasting, Elsevier, vol. 34(2), pages 181-198.
    3. Domenico Ferraro, 2018. "The Asymmetric Cyclical Behavior of the U.S. Labor Market," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 30, pages 145-162, October.
    4. Congressional Budget Office, 2022. "A Markov-Switching Model of the Unemployment Rate: Working Paper 2022-05," Working Papers 57582, Congressional Budget Office.
    5. Luke Hartigan, 2016. "Testing for Symmetry in Weakly Dependent Time Series," Discussion Papers 2016-18, School of Economics, The University of New South Wales.
    6. Lahiri, Kajal & Yang, Liu, 2013. "Forecasting Binary Outcomes," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 1025-1106, Elsevier.
    7. Christoph Gortz & John D. Tsoukalas, 2013. "Learning, Capital Embodied Technology and Aggregate Fluctuations," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 16(4), pages 708-723, October.
    8. Lahiri, Kajal & Peng, Huaming & Zhao, Yongchen, 2015. "Testing the value of probability forecasts for calibrated combining," International Journal of Forecasting, Elsevier, vol. 31(1), pages 113-129.
    9. Andrew C. Chang & Trace J. Levinson, 2023. "Raiders of the lost high‐frequency forecasts: New data and evidence on the efficiency of the Fed's forecasting," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(1), pages 88-104, January.
    10. David G. McMillan, 2009. "Non-linear interest rate dynamics and forecasting: evidence for US and Australian interest rates," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 14(2), pages 139-155.
    11. Garratt, Anthony & Henckel, Timo & Vahey, Shaun P., 2023. "Empirically-transformed linear opinion pools," International Journal of Forecasting, Elsevier, vol. 39(2), pages 736-753.
    12. Zacharias Psaradakis & Martin Sola, 2003. "On detrending and cyclical asymmetry," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(3), pages 271-289.
    13. Pérez-Alonso Alicia & Di Sanzo Silvestro, 2010. "Unemployment and Hysteresis: A Nonlinear Unobserved Components Approach," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(1), pages 1-29, December.
    14. Nicholas Bloom & Fatih Guvenen & Sergio Salgado, 2016. "Skewed Business Cycles," 2016 Meeting Papers 1621, Society for Economic Dynamics.
    15. Khurshid Kiani, 2005. "Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 65-89, August.
    16. Stevens, Philip Andrew, 2007. "Skill shortages and firms' employment behaviour," Labour Economics, Elsevier, vol. 14(2), pages 231-249, April.
    17. Arai, Natsuki, 2020. "Investigating the inefficiency of the CBO’s budgetary projections," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1290-1300.
    18. Mariola Piłatowska & Dorota Witkowska, 2022. "Gender Segregation at Work over Business Cycle—Evidence from Selected EU Countries," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    19. Skalin, Joakim & Teräsvirta, Timo, 2002. "Modeling Asymmetries And Moving Equilibria In Unemployment Rates," Macroeconomic Dynamics, Cambridge University Press, vol. 6(2), pages 202-241, April.
    20. Ahn, Eun S. & Lee, Jin Man, 2012. "The Performance Of Nonlinearity Tests On Asymmetric Nonlinear Time Series," The Journal of Economic Asymmetries, Elsevier, vol. 9(2), pages 11-44.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:intfor:v:35:y:2019:i:4:p:1613-1626. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.