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MULTIPLE EQUATION SYSTEMS WITH STATIONARY ERRORS.

The article reports on the development of an efficient estimation method when there is no set of independent random vectors and to prove some asymptotic theorems concerning the results. When a variable is assumed to be Gaussian, the likelihood function is established and maximized. The asymptotic di...

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Published in:Econometrica Vol. 41; no. 2; pp. 299 - 321
Main Authors: Hannan, E. J., Terrell, R. D.
Format: Article
Published: Wiley-Blackwell Mar1973
Subjects:
Asymptotic expansions
Difference equations
Stochastic convergence
Estimation theory
Distribution (Probability theory)
Central limit theorem
Numerical analysis
Mathematical functions
Probability theory
Online Access:View this record in EBSCOhost
Summary:The article reports on the development of an efficient estimation method when there is no set of independent random vectors and to prove some asymptotic theorems concerning the results. When a variable is assumed to be Gaussian, the likelihood function is established and maximized. The asymptotic distribution for the maximum likelihood estimates can be found by removing the Gaussian assumption. The estimates are regarded as efficient if they had the same asymptotic distribution as the maximum likelihood estimates.