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

Carlo Bianchi, Jean-Louis Brillet, Giorgio Calzolari and Lorenzo Panattoni

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Bootstrap; analytic simulation; Monte Carlo; stochastic simulation; macroeconometric model; French economy (search for similar items in EconPapers)
JEL-codes: C53 C63 (search for similar items in EconPapers)
Date: 1987-02
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in INSEE, Paris, France Paper presented at the Seminaire d'Econometrie de Malinvaud (1987): pp. 1-19

Downloads: (external link)
https://mpra.ub.uni-muenchen.de/24541/1/MPRA_paper_24541.pdf original version (application/pdf)

Related works:
Journal Article: Measuring forecast uncertainty: A review with evaluation based on a macro model of the French economy (1987)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:24541

Access Statistics for this paper

More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Joachim Winter ().