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Forecasting with Econometric Methods: Folklore Versus Fact

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  • Armstrong, J. Scott

Abstract

Evidence from social psychology suggests that econometricians will avoid evidence that disconfirms their beliefs. Two beliefs of econometricians were examined: (1) Econometric methods provide more accurate short-term forecasts than do other methods; and (2) more complex econometric methods yield more accurate forecasts. A survey of 21 experts in econometrics found that 95% agreed with the first statement and 72% agreed with the second. A review of the published empirical evidence yielded little support for either of the two statements in the 41 studies. The method of multiple hypotheses was suggested as a research strategy that will lead to more effective use of disconfirming evidence. Although this strategy was suggested in 1890, it has only recently been used by econometricians.

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  • Armstrong, J. Scott, 1978. "Forecasting with Econometric Methods: Folklore Versus Fact," MPRA Paper 81672, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:81672
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    Cited by:

    1. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    2. Simon Glöser-Chahoud & Johannes Hartwig & I. David Wheat & Martin Faulstich, 2016. "The cobweb theorem and delays in adjusting supply in metals' markets," System Dynamics Review, System Dynamics Society, vol. 32(3-4), pages 279-308, July.
    3. Green, Kesten C. & Armstrong, J. Scott, 2015. "Simple versus complex forecasting: The evidence," Journal of Business Research, Elsevier, vol. 68(8), pages 1678-1685.
    4. J. Scott Armstrong, 1984. "Forecasting by Extrapolation: Conclusions from 25 Years of Research," Interfaces, INFORMS, vol. 14(6), pages 52-66, December.
    5. Armstrong, J. Scott, 1983. "Strategic Planning and Forecasting Fundamentals," MPRA Paper 81682, University Library of Munich, Germany.
    6. Scott Moss & Bruce Edmonds & Steve Wallis, 1997. "Validation and Verification of Computational Models with Multiple Cognitive Agents," Discussion Papers 97-25, Manchester Metropolitan University, Centre for Policy Modelling.
    7. Afshin Amiraslany & Hari S. Luitel & Gerry J. Mahar, 2019. "Structural Breaks, Biased Estimations, and Forecast Errors in a GDP Series of Canada versus the United States," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 25(2), pages 235-244, May.
    8. repec:cup:judgdm:v:10:y:2015:i:5:p:469-478 is not listed on IDEAS
    9. Colin Small & J. Eric Bickel, 2022. "Model Complexity and Accuracy: A COVID-19 Case Study," Decision Analysis, INFORMS, vol. 19(4), pages 354-383, December.
    10. Ashish Sood & Gareth M. James & Gerard J. Tellis, 2009. "Functional Regression: A New Model for Predicting Market Penetration of New Products," Marketing Science, INFORMS, vol. 28(1), pages 36-51, 01-02.
    11. Crystal C. Hall & Daniel M. Oppenheimer, 2015. "Error Parsing: An alternative method of implementing social judgment theory," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 10(5), pages 469-478, September.

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    More about this item

    Keywords

    econometric models; forecasting;

    JEL classification:

    • B23 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Econometrics; Quantitative and Mathematical Studies
    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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