Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/203671 
Year of Publication: 
2019
Series/Report no.: 
DICE Discussion Paper No. 326
Publisher: 
Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE), Düsseldorf
Abstract: 
This paper investigates and extends the computationally attractive nonparametric random coefficients estimator of Fox, Kim, Ryan, and Bajari (2011). We show that their estimator is a special case of the nonnegative LASSO, explaining its sparse nature observed in many applications. Recognizing this link, we extend the estimator, transforming it to a special case of the nonnegative elastic net. The extension improves the estimator's recovery of the true support and allows for more accurate estimates of the random coefficients' distribution. Our estimator is a generalization of the original estimator and therefore, is guaranteed to have a model fit at least as good as the original one. A theoretical analysis of both estimators' properties shows that, under conditions, our generalized estimator approximates the true distribution more accurately. Two Monte Carlo experiments and an application to a travel mode data set illustrate the improved performance of the generalized estimator.
Subjects: 
Random Coefficients
Mixed Logit
Nonparametric Estimation
Elastic Net
JEL: 
C14
C25
L
ISBN: 
978-3-86304-325-4
Document Type: 
Working Paper

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