Evaluating Forecast Uncertainty in Econometric Models: The Effect of Alternative Estimators of Maximum Likelihood Covariance Matrix

Giorgio Calzolari and Lorenzo Panattoni

MPRA Paper from University Library of Munich, Germany

Abstract: Most of the methods proposed in the literature for evaluating forecast uncertainty in econometric models need an estimate of the structural coefficiencs covariance matrix among input data. When estimation is performed with full information maximum likelihood, alternative estimators of such a covariance matrix (Hessian, outer product, generalized least squares type matrix, quasi maximum likelihood type matrix), although asymptotically equ1valent, often produce large differences in practical applications. Experimental results will be given for some econometric models well known in the literature, both with hiscorical data and with data generated by Monte Carlo.

Keywords: Econometric models; simultaneous equations; maximum likelihood; covariance matrix; standard error of forecast (search for similar items in EconPapers)
JEL-codes: C3 C63 (search for similar items in EconPapers)
Date: 1984-07-08
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Citations: View citations in EconPapers (1)

Published in paper presented at The Fourth International Symposium on Forecasting. London Business School, July 8-11 (1984): pp. 1-33

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