Positive time series regression models: theoretical and computational aspects

Taiane Schaedler Prass (), Guilherme Pumi (), Cleiton Guollo Taufemback () and Jonas Hendler Carlos ()
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Taiane Schaedler Prass: Universidade Federal do Rio Grande do Sul
Guilherme Pumi: Universidade Federal do Rio Grande do Sul
Cleiton Guollo Taufemback: Universidade Federal do Rio Grande do Sul
Jonas Hendler Carlos: Universidade Federal do Rio Grande do Sul

Computational Statistics, 2025, vol. 40, issue 3, No 3, 1185-1215

Abstract: Abstract This paper discusses dynamic ARMA-type regression models for positive time series, which can handle bounded non-Gaussian time series without requiring data transformations. Our proposed model includes a conditional mean modeled by a dynamic structure containing autoregressive and moving average terms, time-varying covariates, unknown parameters, and link functions. Additionally, we present the PTSR package and discuss partial maximum likelihood estimation, asymptotic theory, hypothesis testing inference, diagnostic analysis, and forecasting for a variety of regression-based dynamic models for positive time series. A Monte Carlo simulation and a real data application are provided.

Keywords: Non-gaussian time series; Partial maximum likelihood; Regression models; Time series analysis (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00180-024-01531-z

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