期刊
STATISTICAL METHODS AND APPLICATIONS
卷 -, 期 -, 页码 -出版社
SPRINGER HEIDELBERG
DOI: 10.1007/s10260-022-00655-0
关键词
Count data; Penalized estimation; Integer-valued autoregressions; Innovation distribution; Validation
资金
- Projekt DEAL
This paper investigates popular models for time series of count data and proposes an improved estimation approach to enhance accuracy. The approach exploits the smoothness of the innovation distribution and utilizes penalized estimation to optimize model fits.
Popular models for time series of count data are integer-valued autoregressive (INAR) models, for which the literature mainly deals with parametric estimation. In this regard, a semiparametric estimation approach is a remarkable exception which allows for estimation of the INAR models without any parametric assumption on the innovation distribution. However, for small sample sizes, the estimation performance of this semiparametric estimation approach may be inferior. Therefore, to improve the estimation accuracy, we propose a penalized version of the semiparametric estimation approach, which exploits the fact that the innovation distribution is often considered to be smooth, i.e. two consecutive entries of the PMF differ only slightly from each other. This is the case, for example, in the frequently used INAR models with Poisson, negative binomially or geometrically distributed innovations. For the data-driven selection of the penalization parameter, we propose two algorithms and evaluate their performance. In Monte Carlo simulations, we illustrate the superiority of the proposed penalized estimation approach and argue that a combination of penalized and unpenalized estimation approaches results in overall best INAR model fits.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据