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Measuring forecast uncertainty : A review with evaluation based on a macro model of the French economy

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  • Bianchi, Carlo
  • Calzolari, Giorgio
  • Brillet, Jean-Louis

Abstract

Five alternative techniques have been applied to measure the degree of uncertainty associated with the forecasts produced by a macro-model of the French economy, the Mini-DMS developed at INSEE. They are bootstrap, analytic simulation on coefficients, Monte Carlo on coefficients, parametric stochastic simulation and re-estimation, a residual-based procedure. Due to the complexity and the size of the model (nonlinear and with more than 200 equations), several associated technical problems had to be solved. The remarkable convergence of results which has been obtained for all the main endogenous variables suggests that forecast confidence intervals are likely to be quite reliable for this model.
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  • Bianchi, Carlo & Calzolari, Giorgio & Brillet, Jean-Louis, 1987. "Measuring forecast uncertainty : A review with evaluation based on a macro model of the French economy," International Journal of Forecasting, Elsevier, vol. 3(2), pages 211-227.
    • Handle: RePEc:eee:intfor:v:3:y:1987:i:2:p:211-227
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    Cited by:

    1. Gajda, Jan B. & Markowski, Aleksander, 1998. "Model Evaluation Using Stochastic Simulations: The Case of the Econometric Model KOSMOS," Working Papers 61, National Institute of Economic Research.
    2. Bianchi, Carlo & Calzolari, Giorgio & Brillet, Jean-Louis, 1987. "Measuring forecast uncertainty : A review with evaluation based on a macro model of the French economy," International Journal of Forecasting, Elsevier, vol. 3(2), pages 211-227.
    3. Neil R. Ericsson & Jaime Marquez, 1998. "A framework for economic forecasting," Econometrics Journal, Royal Economic Society, vol. 1(Conferenc), pages 228-266.
    4. Calzolari, Giorgio, 1992. "Stima delle equazioni simultanee non-lineari: una rassegna [Estimation of nonlinear simultaneous equations: a survey]," MPRA Paper 24123, University Library of Munich, Germany, revised 1992.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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