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

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Author Info

  • 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.

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File URL: http://mpra.ub.uni-muenchen.de/21287/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 21287.

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Date of creation: 1976
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Handle: RePEc:pra:mprapa:21287

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Related research

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

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References

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  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.
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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, Elsevier, vol. 6(3), pages 317-326, October.
  3. Ray Fair, 2002. "Bootstrapping Macroeconometric Models," Yale School of Management Working Papers, Yale School of Management ysm254, Yale School of Management, revised 01 Aug 2007.
  4. Fair, Ray C., 1986. "Evaluating the predictive accuracy of models," Handbook of Econometrics, Elsevier, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 3, chapter 33, pages 1979-1995 Elsevier.
  5. 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.
  6. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(2), pages 355-78, June.
  7. Calzolari, Giorgio & Corsi, Paolo, 1977. "Stochastic simulation as a validation tool for econometric models," MPRA Paper 21226, University Library of Munich, Germany.
  8. Calzolari, Giorgio, 1987. "La varianza delle previsioni nei modelli econometrici
    [Forecast variance in econometric models]
    ," MPRA Paper 23866, University Library of Munich, Germany.
  9. 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.
  10. Calzolari, Giorgio & Panattoni, Lorenzo, 1988. "Il problema della coerenza delle previsioni nei modelli econometrici non lineari
    [The coherency problem when forecasting with nonlinear econometric models]
    ," MPRA Paper 23904, University Library of Munich, Germany.
  11. 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.
  12. Gajda, Jan B. & Markowski, Aleksander, 1998. "Model Evaluation Using Stochastic Simulations: The Case of the Econometric Model KOSMOS," Working Paper, National Institute of Economic Research 61, National Institute of Economic Research.

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