Estimation in semiparametric quantile factor models

descriptionPublicationkeyboard_double_arrow_right Report , Article , Preprint , Other literature type , Research 01 Jan 2017Embargo end date: 01 Jan 2017Publisher:The IFSJournal:SSRN Electronic Journal (eissn: 1556-5068,

Copyright policy )Funded by:NSF | New Nonparametric Modelin..., ARC | Discovery Projects - Gran..., ARC | Discovery Projects - Gran... +1 projects

Authors: Ma, Shujie; Linton, Oliver; Gao, Jiti;

doi: 10.1920/wp.cem.2018.0718 , 10.2139/ssrn.2963579 , 10.48550/arxiv.1708.09507

arXiv: 1708.09507

handle: 10419/189695

Estimation in semiparametric quantile factor models

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Abstract

We propose an estimation methodology for a semiparametric quantile factor panel model. 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.

Related Organizations

University of Cambridge
United Kingdom
University of California Riverside
UNIVERSITY OF CAMBRIDGE
University of Cambridge
University of California, Riverside
United States

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Keywords

Methodology (stat.ME), FOS: Computer and information sciences, Fama-French Model, Inference, ddc:330, Sieve Estimation, Cross-Sectional Dependence, Statistics - Methodology

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NSF| New Nonparametric Modeling Methods for High-Dimensional Time Series, ARC| Discovery Projects - Grant ID: DP170104421, ARC| Discovery Projects - Grant ID: DP150101012, NSF| Estimation, model selection and inference in two classes of non- and semi-parametric models for repeated measurements