Time-specific average estimation of dynamic panel regressions

Ba Chu

Studies in Nonlinear Dynamics & Econometrics, 2022, vol. 26, issue 4, 581-616

Abstract: This paper introduces an unbiased estimator based on least squares involving time-specific cross-sectional averages for a first-order panel autoregression with a strictly exogenous covariate. The proposed estimator is straightforward to implement as long as the variables of interest have sufficient time variation. The number of cross-sections (N) and the number of time periods (T) can be large, and there is no restriction on the growth rate of N relative to T. It is demonstrated via both theory and a simulation study that the estimator is asymptotically unbiased, and it can provide correct empirical coverage probabilities for the ‘true’ coefficients of the model for various combinations of N and T. An empirical application is also provided to confirm the feasibility of the proposed approach.

Keywords: first difference least squares (FDLS); fixed effects; panel autoregression; pseudo-panel data; time-specific average (TSA) (search for similar items in EconPapers)
JEL-codes: C22 C23 C33 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/snde-2019-0084 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sndecm:v:26:y:2022:i:4:p:581-616:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/snde/html

DOI: 10.1515/snde-2019-0084

Access Statistics for this article

Studies in Nonlinear Dynamics & Econometrics is currently edited by Bruce Mizrach

More articles in Studies in Nonlinear Dynamics & Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().