A1953
Title: Panel data estimation and inference: Homogeneity versus heterogeneity
Authors: Jiti Gao - Monash University (Australia)
Fei Liu - University of Bath (United Kingdom) [presenting]
Bin Peng - Monash University (Australia)
Yayi Yan - Shanghai University of Finance and Economics (China)
Fei Liu - University of Bath (China)
Abstract: An underlying data generating process is defined that allows for different magnitudes of CD, along with time series autocorrelation. This is achieved via high-dimensional moving average processes of infinite order (HDMA($\infty$)). The setup and investigation integrate and enhance homogeneous and heterogeneous panel data estimation and testing in a unified way. To study HDMA($\infty$), the Beveridge-Nelson decomposition is extended to a high-dimensional time series setting, and a complete toolkit is derived. Homogeneity versus heterogeneity is examined using Gaussian approximation, a prevalent technique for establishing uniform inference. For post-testing inference, central limit theorems are derived through Edgeworth expansions for both homogeneous and heterogeneous settings. The practical relevance of the established asymptotic theories is showcased accordingly. Theoretical findings are verified via extensive numerical studies using both simulated and real datasets.
