Functional coefficient quantile regression model with time-varying loadings

Alev Atak, Gabriel Montes-Rojas () and Jose Olmo

Journal of Applied Economics, 2023, vol. 26, issue 1, 2167151

Abstract: This paper proposes a functional coefficient quantile regression model with heterogeneous and time-varying regression coefficients and factor loadings. Estimation of the model coefficients is done in two stages. First, we estimate the unobserved common factors from a linear factor model with exogenous covariates. Second, we plug-in an affine transformation of the estimated common factors to obtain the functional coefficient quantile regression model. The quantile parameter estimators are consistent and asymptotically normal. The application of this model to the quantile process of a cross-section of U.S. firms’ excess returns confirms the predictive ability of firm-specific covariates and the good performance of the local estimator of the heterogeneous and time-varying quantile coefficients.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/15140326.2023.2167151 (text/html)
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:taf:recsxx:v:26:y:2023:i:1:p:2167151

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/recs20

DOI: 10.1080/15140326.2023.2167151

Access Statistics for this article

Journal of Applied Economics is currently edited by Jorge M. Streb

More articles in Journal of Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().