Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions.
We propose flexible Gaussian representations for conditional cumulative distribution functions and give a concave likelihood criterion for their estimation. Optimal representations satisfy the monotonicity property of conditional cumulative distribution functions, including in finite samples and und...
| Publicado en: | Econometrica Vol. 93; no. 5; pp. 1885 - 1914 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Wiley-Blackwell
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=188002704&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 188002704 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00129682 ECN jtl: Econometrica issn: 00129682 maglogo: Y pubinfo: dt: Sep2025 vid: 93 iid: 5 pid: 480 pub: Wiley-Blackwell artinfo: ui: 188002704 10.3982/ECTA19153 ppf: 1885 ppct: 29 formats: tig: atl: Gaussian Transforms Modeling and the Estimation of Distributional Regression Functions. aug: au: Spady, Richard H. Stouli, Sami affil: Department of Economics, Johns Hopkins University Nuffield College, University of Oxford School of Economics, University of Bristol Department of Economics, University of Melbourne su: Cumulative distribution function Maximum likelihood statistics Quantiles Statistical models Gender wage gap sug: subj: Cumulative distribution function Maximum likelihood statistics Quantiles Statistical models Gender wage gap keyword: Conditional density estimation conditional distributions conditional quantiles convexity gender wage gap maximum likelihood misspecification monotonicity Conditional density estimation conditional distributions conditional quantiles convexity gender wage gap maximum likelihood misspecification monotonicity ab: We propose flexible Gaussian representations for conditional cumulative distribution functions and give a concave likelihood criterion for their estimation. Optimal representations satisfy the monotonicity property of conditional cumulative distribution functions, including in finite samples and under general misspecification. We use these representations to provide a unified framework for the flexible maximum likelihood estimation of conditional density, cumulative distribution, and quantile functions at parametric rate. Our formulation yields substantial simplifications and finite sample improvements over related methods. An empirical application to the gender wage gap in the United States illustrates our framework. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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