Variable selection in sparse GLARMA models

TL;DR

This paper introduces a new two-stage variable selection method for sparse GLARMA models, improving efficiency and accuracy in identifying relevant variables in discrete-valued time series.

Contribution

The paper presents a novel iterative approach combining ARMA coefficient estimation with regularized regression for variable selection in sparse GLARMA models.

Findings

Method outperforms alternatives in coefficient recovery

Low computational load of the proposed approach

Consistent estimation of ARMA coefficients in specific cases

Abstract

In this paper, we propose a novel and efficient two-stage variable selection approach for sparse GLARMA models, which are pervasive for modeling discrete-valued time series. Our approach consists in iteratively combining the estimation of the autoregressive moving average (ARMA) coefficients of GLARMA models with regularized methods designed for performing variable selection in regression coefficients of Generalized Linear Models (GLM). We first establish the consistency of the ARMA part coefficient estimators in a specific case. Then, we explain how to efficiently implement our approach. Finally, we assess the performance of our methodology using synthetic data and compare it with alternative methods. Our approach is very attractive since it benefits from a low computational load and is able to outperform the other methods in terms of coefficient estimation, particularly in recovering the non null regression coefficients.