Although the statistical literature extensively covers continuous-valued time series processes and their parametric, non-parametric and semiparametric estimation, the literature on count data time series is considerably less advanced. Among the count data time series models, the integer-valued autoregressive (INAR) model is arguably the most popular one finding applications in a wide variety of fields such as medical sciences, environmentology and economics. While many contributions have been made during the last decades, the majority of the literature focuses on parametric INAR models and estimation techniques. Our emphasis is on the complex but efficient and non-restrictive semiparametric estimation of INAR models. The appeal of this approach lies in the absence of a commitment to a parametric family of innovation distributions. In this paper, we describe the need and the features of our R package spINAR which combines semiparametric simulation, estimation and bootstrapping of INAR models also covering its parametric versions.

翻译：尽管统计文献广泛涉及连续值时间序列过程及其参数、非参数与半参数估计方法，但关于计数数据时间序列的研究则相对滞后。在计数数据时间序列模型中，自回归整数值（INAR）模型堪称为最受欢迎的模型之一，广泛应用于医学、环境经济学等多个领域。尽管过去几十年已取得诸多进展，但现有文献主要聚焦于参数化INAR模型及其估计技术。本文重点探讨复杂但高效且无约束的INAR模型半参数估计方法。该方法的优势在于无需预先设定创新分布的参数化族。本文阐述了R语言包spINAR的需求与功能特性，该包整合了INAR模型（含其参数化版本）的半参数模拟、估计与自助法实现。