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Mode predictors in nonlinear systems with identities

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  • Calzolari, Giorgio
  • Panattoni, Lorenzo

Abstract

For a nonlinear system of simultaneous equations, the mode of the joint distribution of the endogenous variables in the forecast period is proposed as alternative to the more usual deterministic or mean predictors. A first method follows from maximizing the joint density of a subset of the endogenous variables, corresponding to stochastic equations only (analogously to FIML estimation, where identities are first substituted into stochastic equations). Then a more general approach is developed, which maintains the identities. The model with identities is viewed as a mapping between the space of the random errors and a hypersurface in the space of the endogenous variables; the probability density is defined, and maximization is performed on such a hypersurface. Experimental results on these two mode predictors (and comparisons with deterministic and mean predictors) are provided for a macro model of the Italian economy.

Suggested Citation

  • Calzolari, Giorgio & Panattoni, Lorenzo, 1988. "Mode predictors in nonlinear systems with identities," MPRA Paper 28845, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:28845
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    References listed on IDEAS

    as
    1. Calzolari, Giorgio, 1979. "Antithetic variates to estimate the simulation bias in non-linear models," Economics Letters, Elsevier, vol. 4(4), pages 323-328.
    2. Wallis, Kenneth F., 1982. "'Time-series' versus 'econometric' forecasts : A non-linear regression counterexample," Economics Letters, Elsevier, vol. 10(3-4), pages 309-315.
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    4. 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.
    5. 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.
    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, vol. 21(2), pages 355-378, June.
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    9. 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.
    10. 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.
    11. 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.
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    14. 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.
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    Cited by:

    1. Maximiano Pinheiro & Paulo Esteves, 2012. "On the uncertainty and risks of macroeconomic forecasts: combining judgements with sample and model information," Empirical Economics, Springer, vol. 42(3), pages 639-665, June.

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    More about this item

    Keywords

    Nonlinear econometric models; simultaneous equations; deterministic predictor; mean predictor; joint density function.;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables

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