摘要翻译：
大面板数据分析中横截面依赖程度的准确估计是进一步对所研究数据进行统计分析的关键。将更多具有弱关系（横截面依赖）的数据组合在一起往往会导致降维效率较低和预测效果较差。本文通过一个因子模型描述了大量对象（时间序列）之间的横截面依赖性，并用因子负荷的强度来参数化其程度。针对高维时间序列的降维特性，提出了一种新的联合估计方法。此外，建立了一对估计量的联合渐近分布。仿真结果表明了该估计方法在有限样本性能方面的有效性。研究了标准普尔500在跨国宏观变量和股票收益方面的应用。
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英文标题：
《Estimation of Cross-Sectional Dependence in Large Panels》
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作者：
Jiti Gao, Guangming Pan, Yanrong Yang and Bo Zhang
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  Accurate estimation for extent of cross{sectional dependence in large panel data analysis is paramount to further statistical analysis on the data under study. Grouping more data with weak relations (cross{sectional dependence) together often results in less efficient dimension reduction and worse forecasting. This paper describes cross-sectional dependence among a large number of objects (time series) via a factor model and parameterizes its extent in terms of strength of factor loadings. A new joint estimation method, benefiting from unique feature of dimension reduction for high dimensional time series, is proposed for the parameter representing the extent and some other parameters involved in the estimation procedure. Moreover, a joint asymptotic distribution for a pair of estimators is established. Simulations illustrate the effectiveness of the proposed estimation method in the finite sample performance. Applications in cross-country macro-variables and stock returns from S&P 500 are studied.
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PDF链接：
https://arxiv.org/pdf/1904.06843