TY - JOUR
T1 - Mean group instrumental variable estimation of time-varying large heterogeneous panels with endogenous regressors
AU - Bai, Yu
AU - Marcellino, Massimiliano
AU - Kapetanios, George
N1 - Funding Information:
We would like to thank the editor, the associate editor, three anonymous referees and seminar participants at Capital University of Economics and Business, Bocconi University, Dongbei University of Finance and Economics, 2021 North American Summer Meeting of the Econometric Society, 2021 IAAE Annual Conference, 26th International Panel Data Conference and 7th Continuing Education in Macroeconometrics workshop for their useful comments. Bai and Marcellino thank MIUR-PRIN Bando 2017 prot. 2017TA7TYC for financial support for this research. Bai would also acknowledge financial support from ARC Discovery Project DP210100476.
Funding Information:
We would like to thank the editor, the associate editor, three anonymous referees and seminar participants at Capital University of Economics and Business, Bocconi University, Dongbei University of Finance and Economics, 2021 North American Summer Meeting of the Econometric Society, 2021 IAAE Annual Conference, 26th International Panel Data Conference and 7th Continuing Education in Macroeconometrics workshop for their useful comments. Bai and Marcellino thank MIUR-PRIN Bando 2017 prot. 2017TA7TYC for financial support for this research. Bai would also acknowledge financial support from ARC Discovery Project DP210100476.
Publisher Copyright:
© 2023 EcoSta Econometrics and Statistics
PY - 2023
Y1 - 2023
N2 - The large heterogeneous panel data models are extended to the setting where the heterogenous coefficients are changing over time and the regressors are endogenous. Kernel-based non-parametric time-varying parameter instrumental variable mean group (TVP-IV-MG) estimator is proposed for the time-varying cross-sectional mean coefficients. The uniform consistency is shown and the pointwise asymptotic normality of the proposed estimator is derived. A data-driven bandwidth selection procedure is also proposed. The finite sample performance of the proposed estimator is investigated through a Monte Carlo study and an empirical application on multi-country Phillips curve with time-varying parameters.
AB - The large heterogeneous panel data models are extended to the setting where the heterogenous coefficients are changing over time and the regressors are endogenous. Kernel-based non-parametric time-varying parameter instrumental variable mean group (TVP-IV-MG) estimator is proposed for the time-varying cross-sectional mean coefficients. The uniform consistency is shown and the pointwise asymptotic normality of the proposed estimator is derived. A data-driven bandwidth selection procedure is also proposed. The finite sample performance of the proposed estimator is investigated through a Monte Carlo study and an empirical application on multi-country Phillips curve with time-varying parameters.
KW - Large heterogeneous panels
KW - Mean Group estimator
KW - Non-Parametric Methods
KW - Time-varying parameters
UR - http://www.scopus.com/inward/record.url?scp=85164348063&partnerID=8YFLogxK
U2 - 10.1016/j.ecosta.2023.06.004
DO - 10.1016/j.ecosta.2023.06.004
M3 - Article
AN - SCOPUS:85164348063
SN - 2452-3062
JO - Econometrics and Statistics
JF - Econometrics and Statistics
ER -