异方差
蒙特卡罗方法
数学
维数(图论)
因子分析
一致性(知识库)
推论
主成分分析
趋同(经济学)
拟极大似然
最大似然序列估计
收敛速度
统计
样本量测定
应用数学
样品(材料)
最大似然
计量经济学
似然函数
计算机科学
钥匙(锁)
人工智能
经济
化学
经济增长
色谱法
纯数学
计算机安全
几何学
摘要
An approximate factor model of high dimension has two key features. First, the idiosyncratic errors are correlated and heteroskedastic over both the cross-section and time dimensions; the correlations and heteroskedasticities are of unknown forms. Second, the number of variables is comparable or even greater than the sample size. Thus, a large number of parameters exist under a high-dimensional approximate factor model. Most widely used approaches to estimation are principal component based. This paper considers the maximum likelihood–based estimation of the model. Consistency, rate of convergence, and limiting distributions are obtained under various identification restrictions. Monte Carlo simulations show that the likelihood method is easy to implement and has good finite sample properties.
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