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Volume 10 - March 1978

Contents

Page 9 of 10

IIL. Stochastic Control

II. Contributed Papers

IIM. Estimation and Control

II. Contributed Papers

IIN. Statistical Methods

II. Contributed Papers

III. Workshop on Computer System Modelling

IIIA. Invited Papers

III. Workshop on Computer System Modelling

IIIB. Contributed Papers

III. Workshop on Computer System Modelling

Research Article

  • Asymptotic properties of time domain gaussian estimators

  • R. Kohn
  • Published online by Cambridge University Press:
    01 July 2016, pp. 339-359

  • We consider a general non-linear multivariate time series model which can be parameterized by a finite and fixed number of parameters and which can be rewritten, if necessary, in a form such that the disturbances are stationary martingale differences. Given a series of discrete, equally spaced observations we prove the strong consistency and asymptotic normality of the Gaussian estimators of the parameters, the parameters possibly being subject to non-linear constraints. Because the normal equations are usually highly non-linear it may be difficult to obtain explicit expressions for the Gaussian estimates. To overcome this problem we use a Gauss–Newton type algorithm to obtain a sequence of iterates which converge to, and have the same asymptotic properties as, the Gaussian estimates.

Page 9 of 10