Evaluating Forecast Uncertainty in Econometric Models: The Effect of Alternative Estimators of Maximum Likelihood Covariance Matrix
AbstractMost 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 28806.
Date of creation: 08 Jul 1984
Date of revision:
Econometric models; simultaneous equations; maximum likelihood; covariance matrix; standard error of forecast;
Find related papers by 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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