TY - JOUR

T1 - Energy consumption and gdp: a panel data analysis with multi-level cross-sectional dependence

AU - Rodríguez-Caballero, Carlos Vladimir

N1 - Funding Information: During the beginning of this research, I was visiting the Department of Statistics at the University of Padua as part of the Young Investigator Training Program (YITP) awarded by the Association of Foundations of Banking Origin (ACRI) and the Italian Econometric Association. I wish to give thanks to Massimiliano Caporin for hosting me and for his valuable comments. I also would like to thank Niels Haldrup, Carlos Velasco, Eric Hillebrand, Esther Ruiz, Matteo Barigozzi, Yunus Emre Ergemen, Javier Hualde, the participants in the 25th Annual Symposium of The Society for Nonlinear Dynamics and Econometrics, 40th International Panel Data Conference, 70th European Meeting of the Econometric Society, CREATES, UC3M, Banxico, and ITAM academic seminars for helpful comments and discussions. Finally, I am grateful to the associate editor, and two anonymous referees whose helpful suggestions and constructive comments have led to an improved version of the paper. Publisher Copyright: © 2021 EcoSta Econometrics and Statistics Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2022/7/1

Y1 - 2022/7/1

N2 - A fractionally integrated panel data model with a multi-level cross-sectional dependence is proposed. Such dependence is driven by a factor structure that captures comovements between blocks of variables through top-level factors, and within these blocks by non-pervasive factors. The model can include stationary and non-stationary variables, which makes it flexible enough to analyze relevant dynamics that are frequently found in macroeconomic and financial panels. The estimation methodology is based on fractionally differenced block-by-block cross-sectional averages. Monte Carlo simulations suggest that the procedure performs well in typical samples sizes. This methodology is applied to study the long-run relationship between energy consumption and economic growth. The main results suggest that estimates in some empirical studies may have some positive biases caused by neglecting the presence non-pervasive cross-sectional dependence and long-range dependence processes.

AB - A fractionally integrated panel data model with a multi-level cross-sectional dependence is proposed. Such dependence is driven by a factor structure that captures comovements between blocks of variables through top-level factors, and within these blocks by non-pervasive factors. The model can include stationary and non-stationary variables, which makes it flexible enough to analyze relevant dynamics that are frequently found in macroeconomic and financial panels. The estimation methodology is based on fractionally differenced block-by-block cross-sectional averages. Monte Carlo simulations suggest that the procedure performs well in typical samples sizes. This methodology is applied to study the long-run relationship between energy consumption and economic growth. The main results suggest that estimates in some empirical studies may have some positive biases caused by neglecting the presence non-pervasive cross-sectional dependence and long-range dependence processes.

KW - Cross-sectional dependence

KW - Energy-growth nexus

KW - Long memory

KW - Multi-level factor

UR - http://www.scopus.com/inward/record.url?scp=85101016168&partnerID=8YFLogxK

U2 - 10.1016/j.ecosta.2020.11.002

DO - 10.1016/j.ecosta.2020.11.002

M3 - Journal article

AN - SCOPUS:85101016168

VL - 23

SP - 128

EP - 146

JO - Econometrics and Statistics

JF - Econometrics and Statistics

SN - 2468-0389

IS - 1

ER -