Backward Mean Transformation in Panel Data with Predetermined Regressors

Artūras Juodis

A chapter in Essays in Honor of M. Hashem Pesaran: Panel Modeling, Micro Applications, and Econometric Methodology, 2022, vol. 43B, pp 103-143 from Emerald Group Publishing Limited

Abstract: This chapter analyzes the properties of an alternative least-squares based estimator for linear panel data models with general predetermined regressors. This approach uses backward means of regressors to approximate individual specific fixed effects (FE). The author analyzes sufficient conditions for this estimator to be asymptotically efficient, and argue that, in comparison with the FE estimator, the use of backward means leads to a non-trivial bias-variance tradeoff. The author complements theoretical analysis with an extensive Monte Carlo study, where the author finds that some of the currently available results for restricted AR(1) model cannot be easily generalized, and should be extrapolated with caution.

Keywords: Dynamic panel data; predetermined regressors; bias correction; backward mean transformation; nonstationarity; autoregression; C13; C23 (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... 1-90532021000043B006
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

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:eme:aecozz:s0731-90532021000043b006

DOI: 10.1108/S0731-90532021000043B006

Access Statistics for this chapter

More chapters in Advances in Econometrics from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().