On the statistical analysis of high-dimensional factor models

IF 1.2 3区数学 Q2 STATISTICS & PROBABILITY

Statistical Papers Pub Date : 2024-07-01 DOI:10.1007/s00362-024-01557-x

Junfan Mao, Zhigen Gao, Bing-Yi Jing, Jianhua Guo

引用次数: 0

Abstract

High-dimensional factor models have received much attention with the rapid development in big data. We make several contributions to the asymptotic properties of Quasi Maximum Likelihood estimations (QMLE) as both the sample size T and the variable dimension N go to infinity. First we eliminate one of rather unnatural assumptions on the variance estimates which is commonly assumed in the literature. Secondly, we give unified results on the asymptotic properties of the QMLE, which greatly expand the scope of earlier studies. Simulations are given to illustrate these results.

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关于高维因子模型的统计分析

随着大数据的快速发展，高维因子模型受到了广泛关注。当样本量 T 和变量维数 N 都达到无穷大时，我们对准最大似然估计（QMLE）的渐近性质做出了一些贡献。首先，我们消除了文献中常见的关于方差估计的一个相当不自然的假设。其次，我们给出了 QMLE 的渐近性质的统一结果，大大扩展了早期研究的范围。我们还给出了模拟来说明这些结果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistical Papers 数学-统计学与概率论

CiteScore

2.80

自引率

7.70%

发文量

审稿时长

6-12 weeks

期刊介绍： The journal Statistical Papers addresses itself to all persons and organizations that have to deal with statistical methods in their own field of work. It attempts to provide a forum for the presentation and critical assessment of statistical methods, in particular for the discussion of their methodological foundations as well as their potential applications. Methods that have broad applications will be preferred. However, special attention is given to those statistical methods which are relevant to the economic and social sciences. In addition to original research papers, readers will find survey articles, short notes, reports on statistical software, problem section, and book reviews.