A central limit theorem for sets of probability measures

被引：13
|
作者：
Chen, Zengjing [1 ]
Epstein, Larry G. [2 ,3 ]
机构：
[1] Shandong Univ, Sch Math, Shanda Nan Rd 17, Jinan 250100, Shandong, Peoples R China
[2] Boston Univ, Dept Econ, 270 Bay State Rd, Boston, MA 02215 USA
[3] McGill Univ, Dept Econ, 855 Sherbrooke St W Montreal, Quebec City, PQ H3A 2T7, Canada
来源：
STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 2022年 / 152卷
基金：
国家重点研发计划;
关键词：
Model uncertainty; Ambiguity; Central limit theorem; Backward stochastic differential equation; Random walk; Robustness; LARGE NUMBERS; RISK; AMBIGUITY;
D O I：
10.1016/j.spa.2022.07.003
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
We prove a central limit theorem for a sequence of random variables whose means are ambiguous and vary in an unstructured way. Their joint distribution is described by a set of (suitably equivalent) probability measures. The limit is defined by a backward stochastic differential equation that can be interpreted as modeling an ambiguous continuous-time random walk.(c) 2022 Elsevier B.V. All rights reserved.
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页码：424 / 451
页数：28
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