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 modeling discrete-valued time series, with demonstrated superior performance over existing methods.

Contribution

The paper presents a novel two-stage variable selection approach for sparse GLARMA models, including theoretical consistency results and practical implementation details.

Findings

Method outperforms alternatives in coefficient recovery

Low computational load of the approach

Effective in real-world applications

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, compare it with alternative methods and illustrate it on an example of real-world application. Our approach, which is implemented in the GlarmaVarSel R package and available on the CRAN, 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.