Estimation of Cross-Sectional Dependence in Large Panels

TL;DR

This paper introduces a new method to estimate the level of cross-sectional dependence in large panel data using a factor model, improving accuracy and efficiency in high-dimensional settings.

Contribution

A novel joint estimation approach leveraging dimension reduction techniques for accurately measuring cross-sectional dependence in large panels.

Findings

Effective estimation demonstrated in simulations

Improved accuracy over existing methods

Application to macroeconomic and stock data

Abstract

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.