Empirical likelihood for linear models with spatial errors

TL;DR

This paper develops empirical likelihood ratio tests for linear models with spatial errors, demonstrating their chi-squared limiting distributions to facilitate confidence region construction.

Contribution

It introduces empirical likelihood methods for spatial error models and establishes their asymptotic chi-squared distribution, enabling improved inference.

Findings

Empirical likelihood ratio statistics follow chi-squared distributions.

Confidence regions for model parameters can be constructed using these statistics.

The methods are applicable to linear models with spatial errors.

Abstract

For linear models with spatial errors, the empirical likelihood ratio statistics are constructed for the parameters of the models. It is shown that the limiting distributions of the empirical likelihood ratio statistics are chi-squared distributions, which are used to construct confidence regions for the parameters of the models.