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A Two-Stage Forecasting Model: Exponential Smoothing and Multiple Regression

Author

Listed:
  • Dwight B. Crane

    (Mellon National Bank and Trust Company)

  • James R. Crotty

    (State University of New York at Buffalo)

Abstract

This paper presents a forecasting technique which attempts to combine the advantages of both time series analysis and multiple regression. In this two-stage technique, an exponentially smoothed moving average model is used to forecast values of the dependent variable and/or selected independent variables as desired. These forecasts, along with data for other (lagged) independent variables, are then used as inputs to a multiple regression program. The observations are selected sequentially by the regression model so that each equation is based only upon data which would have been available at the time of the forecast, and the coefficiets of the equation are updated as new information becomes available. The final section of the paper describes a successful application of the two-stage model to a demand deposit forecasting problem.

Suggested Citation

  • Dwight B. Crane & James R. Crotty, 1967. "A Two-Stage Forecasting Model: Exponential Smoothing and Multiple Regression," Management Science, INFORMS, vol. 13(8), pages 501-507, April.
    • Handle: RePEc:inm:ormnsc:v:13:y:1967:i:8:p:b501-b507
    DOI: 10.1287/mnsc.13.8.B501
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    Cited by:

    1. Tsokos, Chris P. & Liles, Albert D., 1987. "Stochastic models for forecasting," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 29(3), pages 291-311.
    2. Nowotarski, Jakub & Raviv, Eran & Trück, Stefan & Weron, Rafał, 2014. "An empirical comparison of alternative schemes for combining electricity spot price forecasts," Energy Economics, Elsevier, vol. 46(C), pages 395-412.
    3. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, vol. 18(3), pages 421-438.
    4. Uniejewski, Bartosz & Maciejowska, Katarzyna, 2023. "LASSO principal component averaging: A fully automated approach for point forecast pooling," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1839-1852.
    5. Ewa Ratuszny, 2015. "Risk Modeling of Commodities using CAViaR Models, the Encompassing Method and the Combined Forecasts," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 15, pages 129-156.
    6. Avci, Ezgi & Ketter, Wolfgang & van Heck, Eric, 2018. "Managing electricity price modeling risk via ensemble forecasting: The case of Turkey," Energy Policy, Elsevier, vol. 123(C), pages 390-403.
    7. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    8. Daud Ali Aser & Esin Firuzan, 2022. "Improving Forecast Accuracy Using Combined Forecasts with Regard to Structural Breaks and ARCH Innovations," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(37), pages 1-25, December.
    9. Nowotarski, Jakub & Liu, Bidong & Weron, Rafał & Hong, Tao, 2016. "Improving short term load forecast accuracy via combining sister forecasts," Energy, Elsevier, vol. 98(C), pages 40-49.
    10. Jakub Nowotarski & Rafał Weron, 2015. "Computing electricity spot price prediction intervals using quantile regression and forecast averaging," Computational Statistics, Springer, vol. 30(3), pages 791-803, September.
    11. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    12. Daouia, Abdelaati & Girard, Stéphane & Stupfler, Gilles, 2021. "ExpectHill estimation, extreme risk and heavy tails," Journal of Econometrics, Elsevier, vol. 221(1), pages 97-117.

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