Nonparametric estimation of the random coefficients model: An elastic net approach

Florian Heiss, Stephan Hetzenecker and Maximilian Osterhaus

No 326, DICE Discussion Papers from Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE)

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.

Keywords: Random Coefficients; Mixed Logit; Nonparametric Estimation; Elastic Net (search for similar items in EconPapers)
JEL-codes: C14 C25 L (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-ecm and nep-ore
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Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Nonparametric Estimation of the Random Coefficients Model: An Elastic Net Approach (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:dicedp:326

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