Quantile Factor Models

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dc.contributor.author Chen, Liang
dc.contributor.author Dolado, Juan José
dc.contributor.author Gonzalo, Jesús
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Economía
dc.date.accessioned 2019-05-06T15:39:05Z
dc.date.available 2019-05-06T15:39:05Z
dc.date.issued 2017-10-03
dc.identifier.issn 2340-5031
dc.identifier.uri http://hdl.handle.net/10016/25299
dc.description.abstract Quantile FactorModels (QFM) represent a new class of factor models for high-dimensional panel data. Unlike Approximate Factor Models (AFM), where only mean-shifting factors can be extracted, QFM also allow to recover unobserved factors shifting other relevant parts of the distributions of observed variables. A quantile regression approach, labeled Quantile Factor Analysis (QFA), is proposed to consistently estimate all the quantile-dependent factors and loadings. Their asymptotic distribution is then derived using a kernel-smoothed version of the QFA estimators. Two consistent model selection criteria, based on information criteria and rank minimization, are developed to determine the number of factors at each quantile. Moreover, in contrast to the conditions required for the use of Principal Components Analysis in AFM, QFA estimation remains valid even when the idiosyncratic errors have heavy-tailed distributions. Three empirical applications (regarding climate, financial and macroeconomic panel data) provide evidence that extra factors shifting quantiles other than the means could be relevant in practice.
dc.description.sponsorship Financial support from the National Natural Science Foundation of China (Grant No.71703089), The Open Society Foundation, The Oxford Martin School, the Spanish Ministerio de Economía y Competitividad (grants ECO2016-78652 andMaria de Maeztu MDM 2014-0431), Bank of Spain (ER grant program), and MadEco-CM (grant S205/HUM-3444) is gratefully acknowledged.
dc.format.extent 50
dc.format.mimetype application/pdf
dc.language.iso eng
dc.relation.ispartofseries UC3M Working Papers. Economics
dc.relation.ispartofseries 17-13
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.other Factor models
dc.subject.other Quantile regression
dc.subject.other Incidental parameters
dc.title Quantile Factor Models
dc.type workingPaper
dc.subject.jel C31
dc.subject.jel C38
dc.subject.jel C33
dc.rights.accessRights openAccess
dc.relation.projectID Gobierno de España. ECO2016-78652
dc.relation.projectID Gobierno de España. MDM2014-0431
dc.relation.projectID Comunidad de Madrid. S205/HUM-3444
dc.type.version Draft
dc.identifier.uxxi DT/0000001580
dc.date.updated 2019-04-20
dc.date.updated 2019-04-20
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