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Standard errors of forecasts in dynamic simulation of nonlinear econometric models: some empirical results

Author

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  • Bianchi, Carlo
  • Calzolari, Giorgio

Abstract

In nonlinear econometric models, the evaluation of forecast errors is usually performed, completely or partially, by resorting to stochastic simulation. However, for evaluating the specific contribution of errors in estimated structural coefficients, several alternative methods have been proposed in the literature. Three of these methods will be compared empirically in this paper through experiments performed on a set of "real world" econometric models of small, medium and large size. This work extends to dynamic simulation of nonlinear econometric models, for which the authors have recently analysed the one-period (static) forecast errors empirically.

Suggested Citation

  • 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.
  • Handle: RePEc:pra:mprapa:22657
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    References listed on IDEAS

    as
    1. Bianchi, Carlo & Calzolari, Giorgio & Corsi, Paolo, 1981. "Standard errors of multipliers and forecasts from structural coefficients with block-diagonal covariance matrix," MPRA Paper 22678, University Library of Munich, Germany, revised 1981.
    2. Yoel Haitovsky & Neil Wallace, 1972. "A Study of Discretionary and Nondiscretionary Monetary and Fiscal Policies in the Context of Stochastic Macroeconometric Models," NBER Chapters,in: Economic Research: Retrospect and Prospect, Volume 1, The Business Cycle Today, pages 261-309 National Bureau of Economic Research, Inc.
    3. Cooper, J Phillip & Fischer, Stanley, 1974. "Monetary and Fiscal Policy in the Fully Stochastic St. Louis Econometric Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 6(1), pages 1-22, February.
    4. Calzolari, Giorgio, 1979. "Antithetic variates to estimate the simulation bias in non-linear models," Economics Letters, Elsevier, vol. 4(4), pages 323-328.
    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. Bianchi, Carlo & Calzolari, Giorgio, 1982. "Evaluating forecast uncertainty due to errors in estimated coefficients: empirical comparison of alternative methods," MPRA Paper 22559, University Library of Munich, Germany.
    7. 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.
    8. Calzolari, Giorgio, 1981. "A Note on the Variance of Ex-Post Forecasts in Econometric Models," Econometrica, Econometric Society, vol. 49(6), pages 1593-1595, November.
    9. Gallant, A. Ronald, 1977. "Three-stage least-squares estimation for a system of simultaneous, nonlinear, implicit equations," Journal of Econometrics, Elsevier, vol. 5(1), pages 71-88, January.
    10. Feldstein, Martin S, 1971. "The Error of Forecast in Econometric Models when the Forecast-Period Exogenous Variables are Stochastic," Econometrica, Econometric Society, vol. 39(1), pages 55-60, January.
    11. Bianchi, Carlo & Calzolari, Giorgio, 1980. "The One-Period Forecast Errors in Nonlinear Econometric Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(1), pages 201-208, February.
    12. Jerry A. Hausman, 1974. "Full Information Instrumental Variables Estimation of Simultaneous Equations Systems," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 641-652 National Bureau of Economic Research, Inc.
    13. Mariano, Roberto S, 1982. "Analytical Small-Sample Distribution Theory in Econometrics: The Simultaneous-Equations Case," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 23(3), pages 503-533, October.
    14. Dhrymes, Phoebus J, 1973. "Restricted and Unrestricted Reduced Forms: Asymptotic Distribution and Relative Efficiency," Econometrica, Econometric Society, vol. 41(1), pages 119-134, January.
    15. Brundy, James M & Jorgenson, Dale W, 1971. "Efficient Estimation of Simultaneous Equations by Instrumental Variables," The Review of Economics and Statistics, MIT Press, vol. 53(3), pages 207-224, August.
    16. Schmidt, Peter, 1974. "The Asymptotic Distribution of Forecasts in the Dynamic Simulation of an Econometric Model," Econometrica, Econometric Society, vol. 42(2), pages 303-309, March.
    17. James M. Brundy & Dale W. Jorgenson, 1971. "Efficient estimation of simultaneous equations by instrumental variables," Working Papers in Applied Economic Theory 3, Federal Reserve Bank of San Francisco.
    18. Bianchi, Carlo & Calzolari, Giorgio & Sartori, Franco, 1982. "Stime 2SLS con componenti principali di un modello non lineare dell' economia italiana
      [2SLS with principal components: estimation of a nonlinear model of the Italian economy]
      ," MPRA Paper 22665, University Library of Munich, Germany, revised 1982.
    19. Mariano, Roberto S & Brown, Bryan W, 1983. "Asymptotic Behavior of Predictors in a Nonlinear Simultaneous System," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 24(3), pages 523-536, October.
    20. Hendry, David F. & Harrison, Robin W., 1974. "Monte Carlo methodology and the small sample behaviour of ordinary and two-stage least squares," Journal of Econometrics, Elsevier, vol. 2(2), pages 151-174, July.
    21. James M. Brundy & Dale W. Jorgenson, 1974. "The Relative Efficiency of Instrumental Variables Estimators of Systems of Simultaneous Equations," NBER Chapters,in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 679-700 National Bureau of Economic Research, Inc.
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    Citations

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    Cited by:

    1. 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.
    2. Calzolari, Giorgio, 1987. "La varianza delle previsioni nei modelli econometrici
      [Forecast variance in econometric models]
      ," MPRA Paper 23866, University Library of Munich, Germany.
    3. Bianchi, Carlo & Calzolari, Giorgio, 1983. "Confidence intervals of forecasts from nonlinear econometric models," MPRA Paper 29025, University Library of Munich, Germany.
    4. Bianchi, Carlo & Calzolari, Giorgio & Weihs, Claus, 1986. "Parametric and nonparametric Monte Carlo estimates of standard errors of forecasts in econometric models," MPRA Paper 29120, University Library of Munich, Germany.

    More about this item

    Keywords

    Nonlinear econometric models; forecast; Monte Carlo; standard errors;

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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