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

SERIES Working papers N. 03/2019

56 Pages Posted: 18 Jun 2019

See all articles by George Kapetanios

George Kapetanios

King's College, London

Laura Serlenga

Università degli Studi di Bari “Aldo Moro” (UNIBA) - Dipartimento di Economia e Finanza

Yongcheol Shin

Independent

Date Written: June 2019

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-sectional 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 the associated asymptotic theory, including new nonparametric variance estimators. The validity of the proposed approach is confirmed by Monte Carlo simulation studies. We also demonstrate the empirical usefulness of the proposed approach 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

JEL Classification: C13, C33, F14, F45

Suggested Citation

Kapetanios, George and Serlenga, Laura and Shin, Yongcheol, Estimation and Inference for Multi-dimensional Heterogeneous Panel Datasets with Hierarchical Multi-factor Error Structure (June 2019). SERIES Working papers N. 03/2019, Available at SSRN: https://ssrn.com/abstract=3401751 or http://dx.doi.org/10.2139/ssrn.3401751

George Kapetanios

King's College, London ( email )

30 Aldwych
London, WC2B 4BG
United Kingdom
+44 20 78484951 (Phone)

Laura Serlenga (Contact Author)

Università degli Studi di Bari “Aldo Moro” (UNIBA) - Dipartimento di Economia e Finanza ( email )

Piazza Umberto I
Bari, 70121
Italy

Yongcheol Shin

Independent

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
14
Abstract Views
220
PlumX Metrics