Arbeitspapier
Nonparametric maximum likelihood methods for binary response models with random coefficients
Single index linear models for binary response with random coefficients have been extensively employed in many econometric settings under various parametric specifications of the distribution of the random coefficients. Nonparametric maximum likelihood estimation (NPMLE) as proposed by Cosslett (1983) and Ichimura and Thompson (1998), in contrast, has received less attention in applied work due primarily to computational diffi culties. We propose a new approach to computation of NPMLEs for binary response models that signi cantly increase their computational tractability thereby facilitating greater exibility in applications. Our approach, which relies on recent developments involving the geometry of hyperplane arrangements, is contrasted with the recently proposed deconvolution method of Gautier and Kitamura (2013). An application to modal choice for the journey to work in the Washington DC area illustrates the methods.
- Sprache
-
Englisch
- Erschienen in
-
Series: cemmap working paper ; No. CWP65/18
- Klassifikation
-
Wirtschaft
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Gu, Jiaying
Koenker, Roger
- Ereignis
-
Veröffentlichung
- (wer)
-
Centre for Microdata Methods and Practice (cemmap)
- (wo)
-
London
- (wann)
-
2018
- DOI
-
doi:10.1920/wp.cem.2018.6518
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:44 MEZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Gu, Jiaying
- Koenker, Roger
- Centre for Microdata Methods and Practice (cemmap)
Entstanden
- 2018
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