標(biāo)題：Quasi maximum likelihood estimation for large-dimensional matrix factor models
報(bào)告時(shí)間：2024年07月04日（星期四）10:00-11:00
報(bào)告地點(diǎn)：人民大街校區(qū)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院415報(bào)告廳
主講人：袁超鳳
主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院
報(bào)告內(nèi)容簡(jiǎn)介：
In this study, we introduces a novel approach, called the quasi maximum likelihood estimation (Q-MLE), for estimating large-dimensional matrix factor models. In contrast to the principal components based approach, Q-MLE takes into account heteroskedasticities of the diosyncratic error term, which are simultaneously estimated with other parameters. Theoretical analysis shows that the Q-MLE estimator of the factor loading matrices achieves faster convergence rates than most existing estimators under similar conditions. We also present the asymptotic distributions of the Q-MLE estimators. Extensive numerical experiments demonstrate that the Q-MLE method performs better empirically, especially when heteroscedasticity exists. Furthermore, two real examples in finance and macroeconomics reveal factor patterns across rows and columns, which coincide with financial, economic, or geographical interpretations.
主講人簡(jiǎn)介：
袁超鳳，黑龍江大學(xué)數(shù)學(xué)科學(xué)學(xué)院，教授、碩士研究生導(dǎo)師。主要研究方向?yàn)楦呔S因子分析、面板數(shù)據(jù)分析及時(shí)間序列分析，研究成果主要發(fā)表在JRSSB、JBES、TEST等統(tǒng)計(jì)學(xué)權(quán)威期刊。
