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Forecasting Performance of Structural Time Series Models

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  • Andrews, Rick L

Abstract

Although theoretical research on the properties of structural time-series models has regularly appeared in the literature, there is as yet scant evidence on the forecasting performance of structural models relative to more traditional methods. This study compares the empirical performance of structural time-series models to four methods that are similar in complexity using 111 business and economic time series. The structural approach appears to perform quite well on annual, quarterly, and monthly data, especially for long forecasting horizons and seasonal data. Of the more complex forecasting methods, structural models appear to be one of the most accurate.

Suggested Citation

  • Andrews, Rick L, 1994. "Forecasting Performance of Structural Time Series Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(1), pages 129-133, January.
    • Handle: RePEc:bes:jnlbes:v:12:y:1994:i:1:p:129-33
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    Cited by:

    1. Chen, Yen-Hsiao & Quan, Lianfeng & Liu, Yang, 2013. "An empirical investigation on the temporal properties of China's GDP," China Economic Review, Elsevier, vol. 27(C), pages 69-81.
    2. Sridhar Iyer, 2000. "The relationship between short-term and forward interest rates: a structural time-series analysis," Applied Financial Economics, Taylor & Francis Journals, vol. 10(2), pages 143-153.
    3. Kumar, Saten & Paradiso, Antonio, 2011. "Assessing Sustainability of the Irish Public Debt," MPRA Paper 35295, University Library of Munich, Germany.
    4. Johanna Posch & Fabio Rumler, 2015. "Semi‐Structural Forecasting of UK Inflation Based on the Hybrid New Keynesian Phillips Curve," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(2), pages 145-162, March.
    5. Marcus Cobb, 2009. "Forecasting Chilean Inflation From Disaggregate Components," Working Papers Central Bank of Chile 545, Central Bank of Chile.
    6. Garcia-Ferrer, Antonio & Bujosa-Brun, Marcos, 2000. "Forecasting OECD industrial turning points using unobserved components models with business survey data," International Journal of Forecasting, Elsevier, vol. 16(2), pages 207-227.
    7. Garcia-Ferrer, Antonio & Queralt, Ricardo A., 1998. "Can univariate models forecast turning points in seasonal economic time series?," International Journal of Forecasting, Elsevier, vol. 14(4), pages 433-446, December.
    8. Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.
    9. Walter Labys, 2005. "Commodity Price Fluctuations: A Century of Analysis," Working Papers Working Paper 2005-01, Regional Research Institute, West Virginia University.
    10. Rünstler, Gerhard & Sédillot, Franck, 2003. "Short-term estimates of euro area real GDP by means of monthly data," Working Paper Series 276, European Central Bank.
    11. repec:rri:wpaper:200501 is not listed on IDEAS
    12. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.

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