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Toward a Theory of Evaluating Predictive Accuracy

Author

Listed:
  • Kunst, Robert M.

    (Department of Economics and Finance, Institute for Advanced Studies and Department of Economics, University of Vienna)

  • Jumah, Adusei

    (Department of Economics and Finance, Institute for Advanced Studies and Department of Economics, University of Vienna)

Abstract

We suggest a theoretical basis for the comparative evaluation of forecasts. Instead of the general assumption that the data is generated from a stochastic model, we classify three stages of prediction experiments: pure non-stochastic prediction of given data, stochastic prediction of given data, and double stochastic simulation. The concept is demonstrated using an empirical example of UK investment data.

Suggested Citation

  • Kunst, Robert M. & Jumah, Adusei, 2004. "Toward a Theory of Evaluating Predictive Accuracy," Economics Series 162, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:162
    as

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    File URL: https://irihs.ihs.ac.at/id/eprint/1601
    File Function: First version, 2004
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    References listed on IDEAS

    as
    1. Alvaro Escribano & Santiago Mira, 2002. "Nonlinear error correction models," Journal of Time Series Analysis, Wiley Blackwell, vol. 23(5), pages 509-522, September.
    2. 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.
    3. Clements,Michael & Hendry,David, 1998. "Forecasting Economic Time Series," Cambridge Books, Cambridge University Press, number 9780521632423.
    4. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    5. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
    6. Clements, Michael P. & Hendry, David F., 1998. "Forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 14(1), pages 111-131, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Forecasting; Time series; Investment;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications

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