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Measuring firm performance by using linear and non‐parametric quantile regressions
Author(s) -
Landajo Manuel,
De Andrés Javier,
Lorca Pedro
Publication year - 2008
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/j.1467-9876.2007.00610.x
Subject(s) - quantile , quantile regression , econometrics , parametric statistics , inference , profitability index , mathematics , linear regression , conditional probability distribution , computer science , statistics , economics , artificial intelligence , finance
Summary. Quantile regression models are examined from the standpoint of their suitability to analyse company profitability. Some linear and non‐linear ( B ‐spline) structures are proposed. Linear conditional quantile models provide an intuitive framework which permits conventional statistical inference tools to be applied. Non‐parametric spline‐based quantile regression is a flexible approach, allowing a different grade of curvature for each conditional quantile, thus providing the possibility of capturing certain non‐linear effects that are predicted by economic theory. The behaviour of these variants of the quantile framework is tested on a representative database, which was obtained from the Spanish book publishing industry. Our results confirm the usefulness of the quantile regression approach. Linear models seem to provide suitable descriptions for the behaviour of average performing firms, whereas non‐parametric estimates provide the best fit for the extreme conditional quantiles (i.e. companies which exhibit the highest and the lowest performance in terms of profitability).