The Annals of Applied Probability

Parameter estimation for moving averages with positive innovations

Paul D. Feigin, Marie F. Kratz, and Sidney I. Resnick

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This paper continues the study of time series models generated by nonnegative innovations which was begun by Feigin and Resnick. We concentrate on moving average processes. Estimators for moving average coefficients are proposed and consistency and asymptotic distributions established for the case of an order-one moving average assuming either the right or the left tail of the innovation distribution is regularly varying. The rate of convergence can be superior to that of the Yule-Walker or maximum likelihood estimators.

Article information

Ann. Appl. Probab., Volume 6, Number 4 (1996), 1157-1190.

First available in Project Euclid: 24 October 2002

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Zentralblatt MATH identifier

Primary: 60B10: Convergence of probability measures 60F05: Central limit and other weak theorems 60G55: Point processes 60E20 26F10 62M10: Time series, auto-correlation, regression, etc. [See also 91B84]

Poisson processes linear programming autoregressive processes moving average processes weak convergence consistency time series analysis


Feigin, Paul D.; Kratz, Marie F.; Resnick, Sidney I. Parameter estimation for moving averages with positive innovations. Ann. Appl. Probab. 6 (1996), no. 4, 1157--1190. doi:10.1214/aoap/1035463327.

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