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Coherent optimal prediction with large nonlinear systems: an example based on a French model

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  • Brillet, Jean-Louis
  • Calzolari, Giorgio
  • Panattoni, Lorenzo

Abstract

The drawbacks of predictors obtained with the usual deterministic solution methods in nonlinear systems of stochastic equations have been widely investigated in the literature. Most of the proposed therapies are based on some estimation of the conditional mean of the endogenous variables in the forecast period. This however provides a solution to the problem which does not respect the internal coherency of the model, and in particular does not satisfy nonlinear identities. At the same time, for analogy with univariate skewed distributions, the conditional mean may be expected to lie on the wrong side of the deterministic solution, meaning that it moves towards values of the variables which are less likely to occur, rather than towards the most probable values. Estimation of the most likely joint value of all endogenous variables is proposed as an alternative optimal predictor. Experimentation is performed on a large scale macroeconomic model of the French economy, and some considerations are drawn from the results.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 29057.

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Date of creation: 01 Sep 1986
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Handle: RePEc:pra:mprapa:29057

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Keywords: Macroeconometric model; French economy; mean and mode; joint distribution; coherent prediction;

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References

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  1. Wallis, Kenneth F., 1982. "'Time-series' versus 'econometric' forecasts : A non-linear regression counterexample," Economics Letters, Elsevier, Elsevier, vol. 10(3-4), pages 309-315.
  2. 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.
  3. 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.
  4. Amemiya, Takeshi, 1983. "Non-linear regression models," Handbook of Econometrics, Elsevier, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 1, chapter 6, pages 333-389 Elsevier.
  5. Hall, S G, 1986. "The Application of Stochastic Simulation Techniques to the National Institute's Model 7," The Manchester School of Economic & Social Studies, University of Manchester, University of Manchester, vol. 54(2), pages 180-201, June.
  6. 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.
  7. James M. Brundy & Dale W. Jorgenson, 1971. "Efficient estimation of simultaneous equations by instrumental variables," Working Papers in Applied Economic Theory, Federal Reserve Bank of San Francisco 3, Federal Reserve Bank of San Francisco.
  8. 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 207-24, August.
  9. Fisher, Paul & Salmon, Mark, 1986. "On Evaluating the Importance of Nonlinearity in Large Macroeconometric 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. 27(3), pages 625-46, October.
  10. 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.
  11. Calzolari, Giorgio, 1979. "Antithetic variates to estimate the simulation bias in non-linear models," Economics Letters, Elsevier, Elsevier, vol. 4(4), pages 323-328.
  12. Brown, Bryan W & Mariano, Roberto S, 1984. "Residual-Based Procedures for Prediction and Estimation in a Nonlinear Simultaneous System," Econometrica, Econometric Society, Econometric Society, vol. 52(2), pages 321-43, March.
  13. Bianchi, Carlo & Brillet, Jean-Louis & Calzolari, Giorgio, 1984. "Analyse et mesure de l'incertitude en prevision d'un modele econometrique. Application au modele mini-DMS
    [Analysis and measurement of forecast uncertainty in an econometric model. Application to m
    ," MPRA Paper 22565, University Library of Munich, Germany, revised 1984.
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Citations

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Cited by:
  1. Calzolari, Giorgio & Panattoni, Lorenzo, 1988. "Coherent Forecast with Nonlinear Econometric Models," MPRA Paper 28802, University Library of Munich, Germany.
  2. Calzolari, Giorgio & Panattoni, Lorenzo, 1988. "Mode predictors in nonlinear systems with identities," MPRA Paper 28845, University Library of Munich, Germany.
  3. 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.
  4. Bianchi, Carlo & Brillet, Jean-Louis & Calzolari, Giorgio & Panattoni, Lorenzo, 1987. "Forecast variance in simultaneous equation models: analytic and Monte Carlo methods," MPRA Paper 24541, University Library of Munich, Germany.

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