Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure

George Kapetanios, Laura Serlenga, Yongcheol Shin

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)
12 Downloads (Pure)

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.
Original languageEnglish
Number of pages504
JournalJOURNAL OF ECONOMETRICS
Volume220
Issue number2
Early online date18 Jan 2021
DOIs
Publication statusPublished - Feb 2021

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