IDEAS home Printed from
   My bibliography  Save this article

Penalized Estimation of a Quantile Count Model for Panel Data


  • Matthew Harding
  • Carlos Lamarche


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.

Suggested Citation

  • Matthew Harding & Carlos Lamarche, 2019. "Penalized Estimation of a Quantile Count Model for Panel Data," Annals of Economics and Statistics, GENES, issue 134, pages 177-206.
  • Handle: RePEc:adr:anecst:y:2019:i:134:p:177-206
    DOI: 10.15609/annaeconstat2009.134.0177

    Download full text from publisher

    File URL:
    Download Restriction: no

    More about this item


    Quantile Regression; Penalized Estimation; Count Data; Scanner Data;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:adr:anecst:y:2019:i:134:p:177-206. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Secretariat General) or (Laurent Linnemer). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.