.png){kind=icon}
.png){kind=icon}
Return
Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure
.png){kind=icon}
OA
AI
DOI：10.1016/j.jeconom.2020.04.011.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Given the growing availability of large datasets and following recent research trends on multi-dimensional modelling, we develop three dimensional (3D) panel data models with hierarchical error components that allow for strong cross-section dependence through unobserved heterogeneous global and local factors. We propose consistent estimation procedures by extending the common correlated effects (CCE) estimation approach proposed by Pesaran (2006). The standard CCE approach needs to be modified in order to account for the hierarchical factor structure in 3D panels. Further, we provide asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is confirmed by Monte Carlo simulation studies. We demonstrate the empirical usefulness of the proposed framework through an application to a 3D panel gravity model of bilateral export flows. (c) 2020 Elsevier B.V. All rights reserved.
Keywords:
Multi-dimensional panel data models
Cross-sectional error dependence
Multilateral resistance
The gravity model of bilateral export flows
Unobserved heterogeneous global and local factors
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4
Papers:
5.2K
Citations:
3.0W