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.
- Language
-
Englisch
- Bibliographic citation
-
Series: cemmap working paper ; No. CWP65/18
- Classification
-
Wirtschaft
- Event
-
Geistige Schöpfung
- (who)
-
Gu, Jiaying
Koenker, Roger
- Event
-
Veröffentlichung
- (who)
-
Centre for Microdata Methods and Practice (cemmap)
- (where)
-
London
- (when)
-
2018
- DOI
-
doi:10.1920/wp.cem.2018.6518
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Gu, Jiaying
- Koenker, Roger
- Centre for Microdata Methods and Practice (cemmap)
Time of origin
- 2018
Other Objects (12)
An instrumental variable random coefficients model for binary outcomes
Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach
Nonparametric identification of random coefficients in endogenous and heterogeneous aggregate demand
Nonparametric estimation of the random coefficients model: An elastic net approach
Nonparametric estimation of the random coefficients model: An elastic net approach
Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach
Identification and estimation in a correlated random coefficients binary response model
Nonparametric identification of random coefficients in endogenous and heterogeneous aggregated demand models
Nonparametric identification of a binary random factor in cross section data
Using penalized likelihood to select parameters in a random coefficients multinomial logit model
Using penalized likelihood to select parameters in a random coefficients multinomial logit model
Information bounds and nonparametric maximum likelihood estimation
An instrumental variable random coefficients model for binary outcomes
Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach
Nonparametric identification of random coefficients in endogenous and heterogeneous aggregate demand
Nonparametric estimation of the random coefficients model: An elastic net approach
Nonparametric estimation of the random coefficients model: An elastic net approach
Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach
Identification and estimation in a correlated random coefficients binary response model
Nonparametric identification of random coefficients in endogenous and heterogeneous aggregated demand models
Nonparametric identification of a binary random factor in cross section data
Using penalized likelihood to select parameters in a random coefficients multinomial logit model
Using penalized likelihood to select parameters in a random coefficients multinomial logit model
Information bounds and nonparametric maximum likelihood estimation
An instrumental variable random coefficients model for binary outcomes
Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach
Nonparametric identification of random coefficients in endogenous and heterogeneous aggregate demand
Nonparametric estimation of the random coefficients model: An elastic net approach
Nonparametric estimation of the random coefficients model: An elastic net approach
Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach
Identification and estimation in a correlated random coefficients binary response model
Nonparametric identification of random coefficients in endogenous and heterogeneous aggregated demand models
Nonparametric identification of a binary random factor in cross section data
Using penalized likelihood to select parameters in a random coefficients multinomial logit model
Using penalized likelihood to select parameters in a random coefficients multinomial logit model