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On the efficacy of constraints on the linear combination forecast model

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  • Salvatore Terregrossa

Abstract

Combination forecasting has been demonstrated to be a successful technique for enhanced forecast accuracy of economic and financial variables. An established method to generate the component-forecast weights is the ordinary-least-squares (OLS) regression technique: Actual values of a variable are regressed on within-sample values of forecasts generated by alternative forecast sources. The estimated regression coefficients then serve as weights for out-of-sample combination forecasts. The present study addresses the controversy regarding the efficacy of placing restrictions on the combining model when generating weights for out-of-sample forecasts. Combinations are formed of component earnings-growth forecasts generated separately by financial analysts and a statistical model. Both restricted and unrestricted OLS are used in turn to generate the component-forecast weights. The findings suggest that combinations formed with weights generated by OLS with the constant suppressed and the sum-of-the-coefficients constrained to equal one, lead to enhanced forecast-accuracy and generally perform best. This study differs from a previous related study appearing in Applied Financial Economics1 in at least three main ways: (1) Combination forecasts are formed using actual regression-coefficients as forecast weights; (2) Forecast weights are generated using unrestricted OLS, as well as restricted OLS; (3) All combination forecasts are strictly ex-ante simulated.

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  • Salvatore Terregrossa, 2005. "On the efficacy of constraints on the linear combination forecast model," Applied Economics Letters, Taylor & Francis Journals, vol. 12(1), pages 19-28.
    • Handle: RePEc:taf:apeclt:v:12:y:2005:i:1:p:19-28
    DOI: 10.1080/1350485042000307062
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    1. Newbold, Paul & Zumwalt, J. Kenton & Kannan, Srinivasan, 1987. "Combining forecasts to improve earnings per share prediction : An examination of electric utilities," International Journal of Forecasting, Elsevier, vol. 3(2), pages 229-238.
    2. Lobo, Gerald J., 1991. "Alternative methods of combining security analysts' and statistical forecasts of annual corporate earnings," International Journal of Forecasting, Elsevier, vol. 7(1), pages 57-63, May.
    3. Lobo, Gerald J., 1992. "Analysis and comparison of financial analysts', time series, and combined forecasts of annual earnings," Journal of Business Research, Elsevier, vol. 24(3), pages 269-280, May.
    4. Robert Conroy & Robert Harris, 1987. "Consensus Forecasts of Corporate Earnings: Analysts' Forecasts and Time Series Methods," Management Science, INFORMS, vol. 33(6), pages 725-738, June.
    5. Salvatore Terregrossa, 1999. "Combining analysts' forecasts with causal model forecasts of earnings growth," Applied Financial Economics, Taylor & Francis Journals, vol. 9(2), pages 143-153.
    6. Cooper, J Phillip & Nelson, Charles R, 1975. "The Ex Ante Prediction Performance of the St. Louis and FRB-MIT-PENN Econometric Models and Some Results on Composite Predictors," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 7(1), pages 1-32, February.
    7. Salvatore Terregrossa, 2001. "Robust informational tests on the CAPM," Applied Economics Letters, Taylor & Francis Journals, vol. 8(2), pages 121-124.
    8. Lobo, Gerald J., 1992. "Journal of business research : "Analysis and comparison of financial analysts', time series, and combined forecast of annual earnings", 24 (1992) 269-280," International Journal of Forecasting, Elsevier, vol. 8(4), pages 649-649, December.
    9. William F. Sharpe, 1964. "Capital Asset Prices: A Theory Of Market Equilibrium Under Conditions Of Risk," Journal of Finance, American Finance Association, vol. 19(3), pages 425-442, September.
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