Calzolari, Giorgio and Panattoni, Lorenzo (1984): Evaluating Forecast Uncertainty in Econometric Models: The Effect of Alternative Estimators of Maximum Likelihood Covariance Matrix. Published in: paper presented at The Fourth International Symposium on Forecasting. London Business School, July 811 (8. July 1984): pp. 133.

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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.
Item Type:  MPRA Paper 

Original Title:  Evaluating Forecast Uncertainty in Econometric Models: The Effect of Alternative Estimators of Maximum Likelihood Covariance Matrix 
Language:  English 
Keywords:  Econometric models; simultaneous equations; maximum likelihood; covariance matrix; standard error of forecast 
Subjects:  C  Mathematical and Quantitative Methods > C6  Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63  Computational Techniques ; Simulation Modeling C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables 
Item ID:  28806 
Depositing User:  Giorgio Calzolari 
Date Deposited:  26. Feb 2011 20:25 
Last Modified:  21. Feb 2013 17:58 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/28806 