Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

Identification without assuming mean-stationarity: Quasi ML estimation of dynamic panel models with endogenous regressors.

Kruiniger, H. (2021) 'Identification without assuming mean-stationarity: Quasi ML estimation of dynamic panel models with endogenous regressors.', Econometrics journal, 24 (3). pp. 417-441.

Abstract

Linear GMM estimators for dynamic panel models with predetermined or endogenous regressors suffer from a weak instruments problem when the data are highly persistent. In this paper we propose new random and fixed effects Limited Information Quasi ML estimators (LIQMLEs) for such models. We also discuss LIQMLEs for models that contain time-varying individual effects. Unlike System GMM estimators, the LIQMLEs do not require mean stationarity conditions for consistency. Such conditions often do not hold for the models we consider. Our LIQMLEs are based on a two-step control function approach that includes the first stage model residuals for a predetermined or endogenous regressor in the outcome equation. The LIMLEs are more precise than non-linear GMM estimators that are based on the original outcome equation. The LIQMLEs also compare favourably to various alternative (Q)MLEs in terms of precision, robustness and/or ease of computation.

Item Type:Article
Full text:Publisher-imposed embargo until 12 October 2022.
(AM) Accepted Manuscript
File format - PDF
(442Kb)
Full text:Publisher-imposed embargo until 12 October 2022.
(AM) Accepted Manuscript
File format - PDF (Supplementary materials)
(238Kb)
Full text:Publisher-imposed embargo until 10 December 2022.
(AM) Accepted Manuscript
File format - PDF (Revised version)
(442Kb)
Full text:Publisher-imposed embargo until 10 December 2022.
(AM) Accepted Manuscript
File format - PDF (Revised version supplementary materials)
(239Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1093/ectj/utaa036
Publisher statement:This is a pre-copyedited, author-produced version of an article accepted for publication in The Econometrics Journal following peer review. The version of record is available online at: https://doi.org/10.1093/ectj/utaa036
Date accepted:22 September 2020
Date deposited:28 September 2020
Date of first online publication:12 October 2020
Date first made open access:12 October 2022

Save or Share this output

Export:
Export
Look up in GoogleScholar