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Keywords:
parameter estimation; autoregressive models; white noise; conditional maximum likelihood method; maximum likelihood estimation; iterative method; numerical example; AR model
parameter estimation; autoregressive models; white noise; conditional maximum likelihood method; maximum likelihood estimation; iterative method; numerical example; AR model
Summary:
AR models are frequently used but usually with normally distributed white noise. In this paper AR model with uniformly distributed white noise are introduces. The maximum likelihood estimation of unknown parameters is treated, iterative method for the calculation of estimates is presented. A numerical example of this procedure and simulation results are also given.
References:
[1] G. E. P. Box G. M. Jenkins: Time Series Analysis: Forecasting and Control. Holden-Day, San Francisco 1970. MR 0272138
[2] N. Davies T. Spedding W. Watson: Autoregressive moving average process with non-normal residueals. J. Time Series Anal. 2 (1980), 155-171.
[3] A. J. Lawrence P. A. W. Lewis: The exponential autoregressive moving - average EARMA $(p, q)$ process. J. Roy. Statist. Soc., B 42 (1980), 150-161, MR 0583349
[4] R. D. Martin V. J. Yohai: Robustness in Time Series and Estimating ARMA Models. Proc. Handbook of Statistics 5 Time Series in the Time Domain, Elsevier, Amsterdam 1985. MR 0831746
[5] R. L. Kashyap A. R. Rao: Dynamic Stochastic Models from Empirical Data. Academic Press, New York 1976.
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