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Random switching exponential smoothing and inventory forecasting

Author

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  • Giacomo Sbrana

    (NEOMA Business School)

  • Andrea Silvestrini

    (Bank of Italy, Economic Research Department)

Abstract

Exponential smoothing models are an important prediction tool in macroeconomics, finance and business. This paper presents the analytical forecasting properties of the random coefficient exponential smoothing model in the multiple source of error framework. The random coefficient state-space representation allows for switching between simple exponential smoothing and the local linear trend. Therefore it is possible to control, in a flexible manner, the random changing dynamic behaviour of the time series. The paper establishes the algebraic mapping between the state-space parameters and the implied reduced form ARIMA parameters. In addition, it shows that parametric mapping surmounts the difficulties that are likely to emerge in a direct estimatation of the random coefficient state-space model. Finally, it presents an empirical application comparing the forecast accuracy of the suggested model vis-�-vis other benchmark models, both in the ARIMA and in the Exponential Smoothing class. Using time series relative to wholesalers� inventories in the USA, the out-of-sample results show that the reduced form of the random coefficient exponential smoothing model tends to be superior to its competitors.

Suggested Citation

  • Giacomo Sbrana & Andrea Silvestrini, 2014. "Random switching exponential smoothing and inventory forecasting," Temi di discussione (Economic working papers) 971, Bank of Italy, Economic Research and International Relations Area.
  • Handle: RePEc:bdi:wptemi:td_971_14
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    References listed on IDEAS

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    1. E S Gardner & E McKenzie, 2011. "Why the damped trend works," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(6), pages 1177-1180, June.
    2. Alan S. Blinder & Louis J. Maccini, 1991. "Taking Stock: A Critical Assessment of Recent Research on Inventories," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 73-96, Winter.
    3. Moon, Seongmin & Simpson, Andrew & Hicks, Christian, 2013. "The development of a classification model for predicting the performance of forecasting methods for naval spare parts demand," International Journal of Production Economics, Elsevier, vol. 143(2), pages 449-454.
    4. Sbrana, Giacomo & Silvestrini, Andrea, 2013. "Aggregation of exponential smoothing processes with an application to portfolio risk evaluation," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1437-1450.
    5. Giacomo Sbrana, 2011. "Structural time series models and aggregation: some analytical results," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(3), pages 315-316, May.
    6. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    7. Harvey, Andrew, 2006. "Forecasting with Unobserved Components Time Series Models," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 7, pages 327-412, Elsevier.
    8. Li, Qinyun & Disney, Stephen M. & Gaalman, Gerard, 2014. "Avoiding the bullwhip effect using Damped Trend forecasting and the Order-Up-To replenishment policy," International Journal of Production Economics, Elsevier, vol. 149(C), pages 3-16.
    9. Wen, Yi, 2005. "Understanding the inventory cycle," Journal of Monetary Economics, Elsevier, vol. 52(8), pages 1533-1555, November.
    10. Dekker, Mark & van Donselaar, Karel & Ouwehand, Pim, 2004. "How to use aggregation and combined forecasting to improve seasonal demand forecasts," International Journal of Production Economics, Elsevier, vol. 90(2), pages 151-167, July.
    11. Armstrong, J. Scott, 2006. "Findings from evidence-based forecasting: Methods for reducing forecast error," International Journal of Forecasting, Elsevier, vol. 22(3), pages 583-598.
    12. Blinder, Alan S & Maccini, Louis J, 1991. "The Resurgence of Inventory Research: What Have We Learned?," Journal of Economic Surveys, Wiley Blackwell, vol. 5(4), pages 291-328.
    13. 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.
    14. Nelson, Charles R & Schwert, G William, 1977. "Short-Term Interest Rates as Predictors of Inflation: On Testing the Hypothesis That the Real Rate of Interest is Constant," American Economic Review, American Economic Association, vol. 67(3), pages 478-486, June.
    15. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    16. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    17. McKenzie, Eddie & Gardner Jr., Everette S., 2010. "Damped trend exponential smoothing: A modelling viewpoint," International Journal of Forecasting, Elsevier, vol. 26(4), pages 661-665, October.
    18. Moon, Seongmin & Hicks, Christian & Simpson, Andrew, 2012. "The development of a hierarchical forecasting method for predicting spare parts demand in the South Korean Navy—A case study," International Journal of Production Economics, Elsevier, vol. 140(2), pages 794-802.
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    Cited by:

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    4. Sbrana, Giacomo & Silvestrini, Andrea, 2019. "Random switching exponential smoothing: A new estimation approach," International Journal of Production Economics, Elsevier, vol. 211(C), pages 211-220.
    5. Hamidreza Mirtaheri & Piero Macaluso & Maurizio Fantino & Marily Efstratiadi & Sotiris Tsakanikas & Panagiotis Papadopoulos & Andrea Mazza, 2021. "Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids," Energies, MDPI, vol. 14(21), pages 1-22, November.
    6. Kück, Mirko & Freitag, Michael, 2021. "Forecasting of customer demands for production planning by local k-nearest neighbor models," International Journal of Production Economics, Elsevier, vol. 231(C).
    7. Sbrana, Giacomo & Silvestrini, Andrea, 2022. "Random coefficient state-space model: Estimation and performance in M3–M4 competitions," International Journal of Forecasting, Elsevier, vol. 38(1), pages 352-366.

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    Keywords

    exponential smoothing; ARIMA; inventory; forecasting.;
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