Common correlated effect cross‐sectional dependence corrections for nonlinear conditional mean panel models

Sinem Hacioglu Hoke and George Kapetanios

Journal of Applied Econometrics, 2021, vol. 36, issue 1, 125-150

Abstract: This paper provides an approach to estimation and inference for nonlinear conditional mean panel data models, in the presence of cross‐sectional dependence. We modify Pesaran's (Econometrica, 2006, 74(4), 967–1012) common correlated effects correction to filter out the interactive unobserved multifactor structure. The estimation can be carried out using nonlinear least squares, by augmenting the set of explanatory variables with cross‐sectional averages of both linear and nonlinear terms. We propose pooled and mean group estimators, derive their asymptotic distributions, and show the consistency and asymptotic normality of the coefficients of the model. The features of the proposed estimators are investigated through extensive Monte Carlo experiments. We also present two empirical exercises. The first explores the nonlinear relationship between banks' capital ratios and riskiness. The second estimates the nonlinear effect of national savings on national investment in OECD countries depending on countries' openness.

Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1002/jae.2799

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:wly:japmet:v:36:y:2021:i:1:p:125-150

Ordering information: This journal article can be ordered from
http://www3.intersci ... e.jsp?issn=0883-7252

Access Statistics for this article

Journal of Applied Econometrics is currently edited by M. Hashem Pesaran

More articles in Journal of Applied Econometrics from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley Content Delivery ().