Robust Parameter Estimation of Regression Model with AR(p) Error Terms

TL;DR

This paper proposes a robust estimation method for linear regression models with AR(p) errors assuming a t distribution, using CML and IRA, validated through simulations and real data examples.

Contribution

It introduces a new robust estimation approach for regression with AR(p) errors assuming heavy-tailed t distribution, enhancing model reliability.

Findings

Robust estimators outperform traditional methods in heavy-tailed error scenarios.

Simulation results demonstrate improved parameter accuracy.

Real data applications confirm practical effectiveness.

Abstract

In this paper, we consider a linear regression model with AR(p) error terms with the assumption that the error terms have a t distribution as a heavy tailed alternative to the normal distribution. We obtain the estimators for the model parameters by using the conditional maximum likelihood (CML) method. We conduct an iteratively reweighting algorithm (IRA) to find the estimates for the parameters of interest. We provide a simulation study and three real data examples to illustrate the performance of the proposed robust estimators based on t distribution.