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Confidence intervals of forecasts from nonlinear econometric models

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  • Bianchi, Carlo
  • Calzolari, Giorgio

Abstract

Several methods have been proposed in the last few years for evaluating uncertainty in forecasts produced by nonlinear econometric models. Some methods resort to Monte Carlo, while others resort to different simulation techniques. This work aims at comparing these methods by means of experiments on some econometric models of small, medium and large size, used in practice for forecasting purposes. In most cases of practical interest, direct simulation of confidence intervals allows to overcome the difficulties connected with the nonexistence of finite second order moments, often encountered by the authors when applying Monte Carlo methods to real world models.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 29025.

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Date of creation: 05 Jun 1983
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Handle: RePEc:pra:mprapa:29025

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Keywords: Nonlinear econometric models; stochastic simulation; forecast; confidence intervals;

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References

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  1. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 23(3), pages 503-33, October.
  2. Gallant, A. Ronald, 1977. "Three-stage least-squares estimation for a system of simultaneous, nonlinear, implicit equations," Journal of Econometrics, Elsevier, Elsevier, vol. 5(1), pages 71-88, January.
  3. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(1), pages 201-08, February.
  4. Schmidt, Peter, 1974. "The Asymptotic Distribution of Forecasts in the Dynamic Simulation of an Econometric Model," Econometrica, Econometric Society, Econometric Society, vol. 42(2), pages 303-09, March.
  5. 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.
  6. Ray C. Fair, 1978. "Estimating the Expected Predictive Accuracy of Econometric Models," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 480, Cowles Foundation for Research in Economics, Yale University.
  7. 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, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 24(3), pages 523-36, October.
  8. Calzolari, Giorgio, 1981. "A Note on the Variance of Ex-Post Forecasts in Econometric Models," Econometrica, Econometric Society, Econometric Society, vol. 49(6), pages 1593-95, November.
  9. Calzolari, Giorgio, 1979. "Antithetic variates to estimate the simulation bias in non-linear models," Economics Letters, Elsevier, Elsevier, vol. 4(4), pages 323-328.
  10. 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.
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