A Minimum Distance Estimator for First-Order Autoregressive Processes

Autoregressive Gaussian Random Vectors of First Order

A Modified Weighted Symmetric Estimator for a Gaussian First-Order Autoregressive Model with Additive Outliers

Guttman and Tiao [1], and Chang [2] showed that the effect of outliers may cause serious bias in estimating autocorrelations, partial correlations, and autoregressive moving average parameters (cited in Chang et al. [3]). This paper presents a modified weighted symmetric estimator for a Gaussian first-order autoregressive AR(1) model with additive outliers. We apply the recursive median adjustm...

Distribution of the Least Squares Estimator in a First-Order Autoregressive Model By

SUMMARY This paper investigates the finite sample distribution of the least squares estimator of the autoregressive parameter in a first-order autoregressive model. Uniform asymptotic expansion for the distribution applicable to both stationary and nonstationary cases is obtained. Accuracy of the approximation to the distribution by a first few terms of this expansion is then investigated. It i...

Exact Distribution of the Least Squares Estimator in a First-Order Autoregressive Model By

SUMMARY This paper investigates the finite sample distribution of the least squares estimator of the autoregressive parameter in a first-order autoregressive model. Uniform asymptotic expansion for the distribution applicable to both stationary and nonstationary cases is obtained. Accuracy of approximation to the distribution by a first few terms of this expansion is then investigated. It is fo...

Minimum distance estimation for random coefficient autoregressive models

In this paper, we extend the minimum distance method of Beran (1993) to random coefficient autoregressive (RCA) models. After stating the necessary assumptions the asymptotic properties of the minimum distance estimator are derived. A M S classification: 62M05