A General Method for Third-Order Bias and Variance Corrections on a Nonlinear Estimator

Content Provider	Semantic Scholar
Author	Yang, Zhenlin
Copyright Year	2014
Abstract	Motivated by a recent study of Bao and Ullah (2007a) on finite sample properties of MLE in the pure SAR (spatial autoregressive) model, a general method for third-order bias and variance corrections on a nonlinear estimator is proposed based on stochastic expansion and bootstrap. Working with concentrated estimating equation simplifies greatly the high-order expansions for bias and variance; a simple bootstrap procedure overcomes a major difficulty in analytically evaluating expectations of various quantities in the expansions. The method is then studied in detail using a more general SAR model, with its effectiveness in correcting bias and improving inference fully demonstrated by extensive Monte Carlo experiments. Compared with the analytical approach, the proposed approach is much simpler and has a much wider applicability. The validity of the bootstrap procedure is formally established. The proposed method is then extended to the case of more than one nonlinear estimator.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=2585&context=soe_research
Language	English
Access Restriction	Open
Subject Keyword	Autoregressive model Bootstrapping (statistics) Concentrate Dosage Form Estimated Experiment Greater Than Inference Monte Carlo method Nonlinear programming Nonlinear system Quantity Sample Variance
Content Type	Text
Resource Type	Article