Penalized Estimation of a Quantile Count Model for Panel Data

Matthew Harding and Carlos Lamarche

Annals of Economics and Statistics, 2019, issue 134, 177-206

Abstract: This paper investigates the estimation of a panel quantile model for count data with individual heterogeneity. The method is needed as a result of the increased availability of digital data, which allows us to track event counts at the individual level for a large number of activities from webclicks and retweets to store visits and purchases. We propose a penalized quantile regression estimator and we show that the slope parameter estimator is consistent and asymptotically Gaussian under similar conditions to the ones used in the literature. The penalty serves to shrink individual effects toward zero, improving the performance of fixed effects quantile regression estimators when the time series dimension is small relative to the number of subjects in the panel. We investigate solutions to the computational challenges resulting from the need to estimate tens of thousands of parameters in high-dimensional settings and several simulation studies are carried out to study the small sample performance of the proposed approach. We present a novel empirical application to individual trip counts to the store based on a large panel of food purchase transactions.

Keywords: Quantile Regression; Penalized Estimation; Count Data; Scanner Data (search for similar items in EconPapers)
JEL-codes: C21 C23 C25 C55 (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.jstor.org/stable/10.15609/annaeconstat2009.134.0177 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:adr:anecst:y:2019:i:134:p:177-206

DOI: 10.15609/annaeconstat2009.134.0177

Access Statistics for this article

Annals of Economics and Statistics is currently edited by Laurent Linnemer

More articles in Annals of Economics and Statistics from GENES Contact information at EDIRC.
Bibliographic data for series maintained by Secretariat General () and Laurent Linnemer ().