结构变化的多元回归约束推理

IF 1.3 Q2 STATISTICS & PROBABILITY Statistics & Risk Modeling Pub Date : 2014-01-01 DOI:10.1515/strm-2012-1154

Fuqi Chen, S. Nkurunziza

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引用次数: 0

摘要

本文研究了一类多元回归模型的回归系数的推理问题，该模型具有多个变化点，发生在未知时间，且回归系数可能满足一定的限制条件。假设的限制比最近文献中给出的限制更普遍。在较弱的假设条件下，我们得到了限制估计量和无限制估计量的联合渐近正态性。最后，我们构造了假设约束的检验，并推导了它的渐近幂。

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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Constrained inference in multiple regression with structural changes

In this paper, we study an inference problem for the regression coefcients in some multivariate regression models with multiple change-points occurring at unknown times, when the regression coe cients may satisfy some restrictions. The hypothesized restriction is more general than that given in recent literature. Under a weaker assumption than that given in recent literature, we derive the joint asymptotic normality of the restricted and unrestricted estimators. Finally, we construct a test for the hypothesized restriction and derive its asymptotic power.

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来源期刊

Statistics & Risk Modeling STATISTICS & PROBABILITY-

CiteScore

1.80

自引率

6.70%

发文量

期刊介绍： Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.

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