Accession Number : ADA181184

Title :   M-Estimation for Nearly Non-Stationary Autoregressive Time Series.

Descriptive Note : Technical rept.,

Corporate Author : WASHINGTON UNIV SEATTLE DEPT OF STATISTICS

Personal Author(s) : Cox,Dennis D ; Llatas,Isabel

PDF Url : ADA181184

Report Date : Mar 1987

Pagination or Media Count : 43

Abstract : The nearly nonstationary first order autoregression is a sequence of processes where the autoregressive coefficient tends to 1 as n approaches infinity. M-estimates of the autoregressive coefficient are considered. The process is allowed to be nongaussian, but a 2 + delta moment condition is assumed. The limiting distribution is not the usual normal limit but is characterized as a ratio of two stochastic integrals. The asymptotically most efficient M-estimate is not given by maximum likelihood. However, it is shown that the loss of efficiency in using maximum likelihood is no worse than about 20% whereas the usual least squares estimator can have arbitrarily low efficiency. Keywords: M estimation; time series, autoregressive; non stationary.

Descriptors :   *TIME SERIES ANALYSIS, AUTOMATIC, DISTRIBUTION, EFFICIENCY, ESTIMATES, LIMITATIONS, RATIOS, REGRESSION ANALYSIS, SERIES(MATHEMATICS), COEFFICIENTS, OPTIMIZATION, SEQUENCES(MATHEMATICS)

Subject Categories : Statistics and Probability

Distribution Statement : APPROVED FOR PUBLIC RELEASE