Adaptive estimators for parameters of the autoregression function of a Markov chain

✍ Scribed by Wolfgang Wefelmeyer

• Publisher: Elsevier Science
• Year: 1997
• Tongue: English
• Weight: 446 KB
• Volume: 58
• Category: Article
• ISSN: 0378-3758
• DOI: 10.1016/s0378-3758(96)00079-1

✦ Synopsis

Suppose we observe an ergodic Markov chain on the real line, with a parametric model for the autoregression function, i.e. the conditional mean of the transition distribution. If one specifies, in addition, a parametric model for the conditional variance, one can define a simple estimator for the parameter, the maximum quasi-likelihood estimator. It is robust against misspecification of the conditional variance, but not efficient. We construct an estimator which is adaptive in the sense that it is efficient if the conditional variance is misspecified, and asymptotically as good as the maximum quasi-likelihood estimator if the conditional variance is correctly specified. The adaptive estimator is a weighted nonlinear least-squares estimator, with weights given by predictors for the conditional variance.