Quantile regression for count data: jittering versus regression coefficients modelling in the analysis of credits earned by university students after remote teaching

Viviana Carcaiso () and Leonardo Grilli
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Viviana Carcaiso: University of Padua

Statistical Methods & Applications, 2023, vol. 32, issue 4, No 2, 1082 pages

Abstract: Abstract The extension of quantile regression to count data raises several issues. We compare the traditional approach, based on transforming the count variable using jittering, with a recently proposed approach in which the coefficients of quantile regression are modelled by parametric functions. We exploit both methods to analyse university students’ data to evaluate the effect of emergency remote teaching due to COVID-19 on the number of credits earned by the students. The coefficients modelling approach performs a smoothing that is especially convenient in the tails of the distribution, preventing abrupt changes in the point estimates and increasing precision. Nonetheless, model selection is challenging because of the wide range of options and the limited availability of diagnostic tools. Thus the jittering approach remains fundamental to guide the choice of the parametric functions.

Keywords: COVID-19; Integrated loss function; Quantile regression coefficients modelling (QRCM); R package qrcm; Remote teaching; University credits (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10260-022-00661-2

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