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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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Publicado en:Econometrica Vol. 41; no. 2; pp. 299 - 321
Autores principales: Hannan, E. J., Terrell, R. D.
Formato: Artículo
Publicado: Wiley-Blackwell Mar1973
Materias:
Asymptotic expansions
Difference equations
Stochastic convergence
Estimation theory
Distribution (Probability theory)
Central limit theorem
Numerical analysis
Mathematical functions
Probability theory
Acceso en línea:Ver este registro en EBSCOhost
Sumario: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.