Parametric modeling of quantile regression coefficient functions with count data

Paolo Frumento () and Nicola Salvati
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Paolo Frumento: University of Pisa
Nicola Salvati: University of Pisa

Statistical Methods & Applications, 2021, vol. 30, issue 4, No 7, 1237-1258

Abstract: Abstract Applying quantile regression to count data presents logical and practical complications which are usually solved by artificially smoothing the discrete response variable through jittering. In this paper, we present an alternative approach in which the quantile regression coefficients are modeled by means of (flexible) parametric functions. The proposed method avoids jittering and presents numerous advantages over standard quantile regression in terms of computation, smoothness, efficiency, and ease of interpretation. Estimation is carried out by minimizing a “simultaneous” version of the loss function of ordinary quantile regression. Simulation results show that the described estimators are similar to those obtained with jittering, but are often preferable in terms of bias and efficiency. To exemplify our approach and provide guidelines for model building, we analyze data from the US National Medical Expenditure Survey. All the necessary software is implemented in the existing R package qrcm.

Keywords: Quantile regression (qr); Quantile regression coefficients modeling (qrcm); R package qrcm; NMES data (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s10260-021-00557-7

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