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Improving Unemployment Rate Forecasts Using Survey Data

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  • Österholm, Pär

    () (National Institute of Economic Research)

Abstract

This paper investigates whether forecasts of the Swedish unemployment rate can be improved by using business and household survey data. We conduct an out-of-sample forecast exercise in which the performance of a Bayesian VAR model with only macroeconomic data is compared to that when the model also includes survey data. Results show that the forecasting performance at short horizons can be improved. The im-provement is largest when forward-looking data from the manufacturing industry is employed.

Suggested Citation

  • Österholm, Pär, 2009. "Improving Unemployment Rate Forecasts Using Survey Data," Working Papers 112, National Institute of Economic Research.
  • Handle: RePEc:hhs:nierwp:0112
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    References listed on IDEAS

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    1. Hansson, Jesper & Jansson, Per & Lof, Marten, 2005. "Business survey data: Do they help in forecasting GDP growth?," International Journal of Forecasting, Elsevier, vol. 21(2), pages 377-389.
    2. Armstrong, J. Scott, 2007. "Significance Tests Harm Progress in Forecasting," MPRA Paper 81664, University Library of Munich, Germany.
    3. Pär Österholm, 2008. "Can forecasting performance be improved by considering the steady state? An application to Swedish inflation and interest rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 41-51.
    4. Sydney C. Ludvigson, 2004. "Consumer Confidence and Consumer Spending," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 29-50, Spring.
    5. Todd E. Clark, 2004. "Can out-of-sample forecast comparisons help prevent overfitting?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(2), pages 115-139.
    6. Inoue, Atsushi & Kilian, Lutz, 2006. "On the selection of forecasting models," Journal of Econometrics, Elsevier, vol. 130(2), pages 273-306, February.
    7. Milas, Costas & Rothman, Philip, 2008. "Out-of-sample forecasting of unemployment rates with pooled STVECM forecasts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 101-121.
    8. 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.
    9. Sims, Christopher A., 1992. "Interpreting the macroeconomic time series facts : The effects of monetary policy," European Economic Review, Elsevier, vol. 36(5), pages 975-1000, June.
    10. Ashley, R & Granger, C W J & Schmalensee, R, 1980. "Advertising and Aggregate Consumption: An Analysis of Causality," Econometrica, Econometric Society, vol. 48(5), pages 1149-1167, July.
    11. Berger, Helge & Österholm, Pär, 2009. "Does money still matter for U.S. output?," Economics Letters, Elsevier, vol. 102(3), pages 143-146, March.
    12. John A. Cotsomitis & Andy C. C. Kwan, 2006. "Can Consumer Confidence Forecast Household Spending? Evidence from the European COmmission Business and Consumer Surveys," Southern Economic Journal, Southern Economic Association, vol. 72(3), pages 597-610, January.
    13. Amos Golan & Jeffrey M. Perloff, 2004. "Superior Forecasts of the U.S. Unemployment Rate Using a Nonparametric Method," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 433-438, February.
    14. Todd Clark & Michael McCracken, 2005. "Evaluating Direct Multistep Forecasts," Econometric Reviews, Taylor & Francis Journals, vol. 24(4), pages 369-404.
    15. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    16. Malin Adolfson & Michael K. Andersson & Jesper Lindé & Mattias Villani & Anders Vredin, 2007. "Modern Forecasting Models in Action: Improving Macroeconomic Analyses at Central Banks," International Journal of Central Banking, International Journal of Central Banking, vol. 3(4), pages 111-144, December.
    17. Andy C.C. Kwan & John A. Cotsomitis, 2006. "The Usefulness of Consumer Confidence in Forecasting Household Spending in Canada: A National and Regional Analysis," Economic Inquiry, Western Economic Association International, vol. 44(1), pages 185-197, January.
    18. Antonio Ribba, 2006. "The joint dynamics of inflation, unemployment and interest rate in the United States since 1980," Empirical Economics, Springer, vol. 31(2), pages 497-511, June.
    19. Armstrong, J. Scott, 2007. "Significance tests harm progress in forecasting," International Journal of Forecasting, Elsevier, vol. 23(2), pages 321-327.
    20. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
    21. Magnus Gustavsson & Pär Österholm, 2010. "The presence of unemployment hysteresis in the OECD: what can we learn from out-of-sample forecasts?," Empirical Economics, Springer, vol. 38(3), pages 779-792, June.
    22. Hanson, Michael S., 2004. "The "price puzzle" reconsidered," Journal of Monetary Economics, Elsevier, vol. 51(7), pages 1385-1413, October.
    23. Jared Laxton & Igor Ermolaev & Charles Freedman & Ondrej Kamenik & Michel Juillard & Douglas Laxton & Ioan Carabenciov & Dmitry Korshunov, 2008. "A Small Quarterly Multi-Country Projection Model," IMF Working Papers 08/279, International Monetary Fund.
    24. Franses, Philip Hans & Paap, Richard & Vroomen, Bjorn, 2004. "Forecasting unemployment using an autoregression with censored latent effects parameters," International Journal of Forecasting, Elsevier, vol. 20(2), pages 255-271.
    25. Yang, Shu-Chun Susan, 2007. "Tentative evidence of tax foresight," Economics Letters, Elsevier, vol. 96(1), pages 30-37, July.
    26. Mattias Villani, 2009. "Steady-state priors for vector autoregressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 630-650.
    27. Galina Hale & Òscar Jordà, 2007. "Do monetary aggregates help forecast inflation?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue apr13.
    28. Philip Rothman, 1998. "Forecasting Asymmetric Unemployment Rates," The Review of Economics and Statistics, MIT Press, vol. 80(1), pages 164-168, February.
    29. Norman R. Swanson, 2000. "An Out of Sample Test for Granger Causality," Econometric Society World Congress 2000 Contributed Papers 0362, Econometric Society.
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    1. repec:spr:jbuscr:v:12:y:2016:i:1:d:10.1007_s41549-016-0002-5 is not listed on IDEAS
    2. Robert Lehmann & Antje Weyh, 2016. "Forecasting Employment in Europe: Are Survey Results Helpful?," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(1), pages 81-117, September.

    More about this item

    Keywords

    Bayesian VAR; Labour market;

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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