Nonparametric Tests in Linear Model with Autoregressive Errors

TL;DR

This paper introduces a family of nonparametric tests for linear regression models with autoregressive errors that do not require estimating nuisance parameters, offering a potentially simpler alternative to existing methods.

Contribution

The paper proposes novel nonparametric tests for regression with autoregressive errors that bypass nuisance parameter estimation, improving simplicity and robustness.

Findings

Tests perform well without estimating nuisance parameters

Method reduces complexity compared to existing approaches

Applicable to models with autoregressive error structures

Abstract

In the linear regression model with possibly autoregressive errors, we propose a family of nonparametric tests for regression under a nuisance autoregression. The tests avoid the estimation of nuisance parameters, in contrast to the tests proposed in the literature.