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Divergences in the results of stochastic and deterministic simulation of an Italian non linear econometric model

Author

Listed:
  • Bianchi, Carlo
  • Calzolari, Giorgio
  • Corsi, Paolo

Abstract

The importance of the simulation (both deterministic and stochastic) in the validation process of a non linear econometric model is underlined. Synthetic results of a large set of simulations on a non linear model of the Italian economy are presented. The benefits and the risks of the stochastic simulation are discussed, with particular emphasis on the problem of the existence of divergences in the results of the two methods of simulation.

Suggested Citation

  • Bianchi, Carlo & Calzolari, Giorgio & Corsi, Paolo, 1976. "Divergences in the results of stochastic and deterministic simulation of an Italian non linear econometric model," MPRA Paper 21287, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:21287
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    File URL: https://mpra.ub.uni-muenchen.de/21287/1/MPRA_paper_21287.pdf
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    References listed on IDEAS

    as
    1. Bianchi, Carlo & Calzolari, Giorgio & Corsi, Paolo & Sartori, Franco & Specioso, Isidoro, 1974. "Aggiornamento del modello al 1974 e nuove simulazioni
      [Updating the model and new simulations for 1974]
      ," MPRA Paper 22677, University Library of Munich, Germany, revised 1975.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Bianchi, Carlo & Calzolari, Giorgio & Corsi, Paolo, 1978. "Stochastic simulation of econometric models: installation procedures and user's instructions," MPRA Paper 24173, University Library of Munich, Germany.
    2. Calzolari, Giorgio & Panattoni, Lorenzo, 1990. "Mode predictors in nonlinear systems with identities," International Journal of Forecasting, Elsevier, vol. 6(3), pages 317-326, October.
    3. Fair Ray C, 2003. "Bootstrapping Macroeconometric Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(4), pages 1-26, December.
    4. 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.
    5. Fair, Ray C, 1980. "Estimating the Expected Predictive Accuracy of Econometric Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(2), pages 355-378, June.
    6. Fair, Ray C., 1986. "Evaluating the predictive accuracy of models," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 33, pages 1979-1995 Elsevier.
    7. Calzolari, Giorgio, 1987. "La varianza delle previsioni nei modelli econometrici
      [Forecast variance in econometric models]
      ," MPRA Paper 23866, University Library of Munich, Germany.
    8. Bianchi, Carlo & Calzolari, Giorgio & Corsi, Paolo, 1979. "Some results on the stochastic simulation of a nonlinear model of the Italian economy," MPRA Paper 22684, University Library of Munich, Germany.
    9. Calzolari, Giorgio & Corsi, Paolo, 1977. "Stochastic simulation as a validation tool for econometric models," MPRA Paper 21226, University Library of Munich, Germany.
    10. Bianchi, Carlo & Calzolari, Giorgio, 1983. "Standard errors of forecasts in dynamic simulation of nonlinear econometric models: some empirical results," MPRA Paper 22657, University Library of Munich, Germany, revised 1983.
    11. Brillet, Jean-Louis & Calzolari, Giorgio & Panattoni, Lorenzo, 1986. "Coherent optimal prediction with large nonlinear systems: an example based on a French model," MPRA Paper 29057, University Library of Munich, Germany.

    More about this item

    Keywords

    Stochastic simulation; nonlinear econometric model; divergences of results; model of the Italian economy;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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