Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/314217 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Applied Economics [ISSN:] 1667-6726 [Volume:] 26 [Issue:] 1 [Article No.:] 2167151 [Year:] 2023 [Pages:] 1-38
Publisher: 
Taylor & Francis, Abingdon
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.
Subjects: 
panel data
partially linear regression model
Quantile factor model
time-varying factor loadings
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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