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Estimation and inference for multi-dimensional heterogeneous panel datasets with hierarchical multi-factor error structure

George Kapetanios (), Laura Serlenga () and Yongcheol Shin ()

Journal of Econometrics, 2021, vol. 220, issue 2, 504-531

Abstract: 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.

Keywords: Multi-dimensional panel data models; Cross-sectional error dependence; Unobserved heterogeneous global and local factors; Multilateral resistance; The gravity model of bilateral export flows (search for similar items in EconPapers)
JEL-codes: C13 C33 F14 F45 (search for similar items in EconPapers)
Date: 2021
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Working Paper: Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure (2019) Downloads
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DOI: 10.1016/j.jeconom.2020.04.011

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