Estimation and inference in semiparametric quantile factor models

Shujie Ma, Oliver Linton and Jiti Gao

Journal of Econometrics, 2021, vol. 222, issue 1, 295-323

Abstract: We consider a semiparametric quantile factor panel model that allows observed stock-specific characteristics to affect stock returns in a nonlinear time-varying way, extending Connor, Hagmann, and Linton (2012) to the quantile restriction case. We propose a sieve-based estimation methodology that is easy to implement. We provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term. We apply our method to daily stock return data where we find significant evidence of nonlinearity in many of the characteristic exposure curves.

Keywords: Cross-sectional dependence; Fama–French model; Inference; Quantile; Sieve estimation (search for similar items in EconPapers)
JEL-codes: C12 C13 C14 G12 G14 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Related works:
Working Paper: Estimation and Inference in Semiparametric Quantile Factor Models (2019)
Working Paper: Estimation and inference in semiparametric quantile factor models (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:222:y:2021:i:1:p:295-323

DOI: 10.1016/j.jeconom.2020.07.003

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