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A Latent Variable Approach to Forecasting the Unemployment Rate

Author

Listed:
  • C. L. Chua

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • G. C. Lim

    () (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

  • Sarantis Tsiaplias

    (Melbourne Institute of Applied Economic and Social Research, The University of Melbourne)

Abstract

A forecasting model for unemployment is constructed that exploits the time-series properties of unemployment while satisfying the economic relationships specified by Okun's law and the Phillips curve. In deriving the model, we jointly consider the problem of obtaining estimates of the unobserved potential rate of unemployment consistent with Okun's law and Phillips curve, and associating the potential rate of unemployment to actual unemployment. The empirical example shows that the model clearly outperforms alternative forecasting procedures typically used to forecast unemployment.

Suggested Citation

  • C. L. Chua & G. C. Lim & Sarantis Tsiaplias, 2009. "A Latent Variable Approach to Forecasting the Unemployment Rate," Melbourne Institute Working Paper Series wp2009n19, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
  • Handle: RePEc:iae:iaewps:wp2009n19
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    References listed on IDEAS

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    1. Kuttner, Kenneth N, 1994. "Estimating Potential Output as a Latent Variable," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 361-368, July.
    2. 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.
    3. Anderson, Heather M. & Low, Chin Nam & Snyder, Ralph, 2006. "Single source of error state space approach to the Beveridge Nelson decomposition," Economics Letters, Elsevier, pages 104-109.
    4. David Gruen & Adrian Pagan & Christopher Thompson, 1999. "The Phillips Curve in Australia," RBA Research Discussion Papers rdp1999-01, Reserve Bank of Australia.
    5. 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.
    6. Rapach, David E. & Wohar, Mark E. & Rangvid, Jesper, 2005. "Macro variables and international stock return predictability," International Journal of Forecasting, Elsevier, vol. 21(1), pages 137-166.
    7. Malley, Jim & Molana, Hassan, 2008. "Output, unemployment and Okun's law: Some evidence from the G7," Economics Letters, Elsevier, vol. 101(2), pages 113-115, November.
    8. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    9. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    10. Artis, M. J. & Zhang, W., 1990. "BVAR forecasts for the G-7," International Journal of Forecasting, Elsevier, vol. 6(3), pages 349-362, October.
    11. Harvey, A C, et al, 1986. "Stochastic Trends in Dynamic Regression Models: An Application to the Employment-Output Equations," Economic Journal, Royal Economic Society, vol. 96(384), pages 975-985, December.
    12. Ribeiro Ramos, Francisco Fernando, 2003. "Forecasts of market shares from VAR and BVAR models: a comparison of their accuracy," International Journal of Forecasting, Elsevier, vol. 19(1), pages 95-110.
    13. Hamilton, James D, 2001. "A Parametric Approach to Flexible Nonlinear Inference," Econometrica, Econometric Society, vol. 69(3), pages 537-573, May.
    14. Taylor, James W., 2008. "Exponentially weighted information criteria for selecting among forecasting models," International Journal of Forecasting, Elsevier, vol. 24(3), pages 513-524.
    15. Gruen, David & Pagan, Adrian & Thompson, Christopher, 1999. "The Phillips curve in Australia," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 223-258, October.
    16. repec:gue:guelph:1991-4 is not listed on IDEAS
    17. Attfield, Clifford L. F. & Silverstone, Brian, 1998. "Okun's Law, Cointegration and Gap Variables," Journal of Macroeconomics, Elsevier, pages 625-637.
    18. LeSage, James P, 1990. "A Comparison of the Forecasting Ability of ECM and VAR Models," The Review of Economics and Statistics, MIT Press, pages 664-671.
    19. Ord, J.K. & Koehler, A. & Snyder, R.D., 1995. "Estimation and Prediction for a Class of Dynamic Nonlinear Statistical Models," Monash Econometrics and Business Statistics Working Papers 4/95, Monash University, Department of Econometrics and Business Statistics.
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    Cited by:

    1. Andreas Karatahansopoulos & Georgios Sermpinis & Jason Laws & Christian Dunis, 2014. "Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 596-610, December.
    2. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Georgios Sermpinis & Charalampos Stasinakis & Konstantinos Theofilatos & Andreas Karathanasopoul, 2014. "Inflation and Unemployment Forecasting with Genetic Support Vector Regression," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 471-487, September.

    More about this item

    Keywords

    Forecasting; Unemployment; Unobserved Components;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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