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Forecast variance in simultaneous equation models: analytic and Monte Carlo methods

Bianchi, Carlo and Brillet, Jean-Louis and Calzolari, Giorgio and Panattoni, Lorenzo (1987): Forecast variance in simultaneous equation models: analytic and Monte Carlo methods. Published in: INSEE, Paris, France No. Paper presented at the Seminaire d'Econometrie de Malinvaud (February 1987): pp. 1-19.

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Abstract

Five alternative techniques have been applied to measure the degree of uncertainty associated with the forecasts produced by a macro-model of the French economy, the Mini-DMS developed at INSEE. They are bootstrap, analytic simulation on coefficients, Monte Carlo on coefficients, parametric stochastic simulation and re-estimation, a residual-based procedure. Due to the complexity and the size of the model (nonlinear and with more than 200 equations), several associated technical problems had to be solved. The remarkable convergence of results which has been obtained for all the main endogenous variables suggests that forecast confidence intervals are likely to be quite reliable for this model.

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