The analysis of transformations for profit-and-loss data

Anthony C. Atkinson, Marco Riani and Aldo Corbellini

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: We analyse data on the performance of investment funds, 99 out of 309 of which report a loss, and on the profitability of 1405 firms, 407 of which report losses. The problem in both cases is to use regression to predict performance from sets of explanatory variables. In one case, it is clear from scatter plots of the data that the negative responses have a lower variance than the positive responses and a different relationship with the explanatory variables. Because the data include negative responses, the Box–Cox transformation cannot be used. We develop a robust version of an extension to the Yeo–Johnson transformation which allows different transformations for positive and negative responses. Tests and graphical methods from our robust analysis enable the detection of outliers, the assessment of values of the two transformation parameters and the building of simple regression models. Performance comparisons are made with non-parametric transformations.

Keywords: additivity and variance stabilization; alternating conditional expectations; Box-Cox transformation; constructed variable; fan plot; forward search; linked plots; robust methods; Yeo-Johnson transformation (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2020-04-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published in Journal of the Royal Statistical Society. Series C: Applied Statistics, 1, April, 2020, 69(2), pp. 251 - 275. ISSN: 0035-9254

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Related works:
Journal Article: The analysis of transformations for profit‐and‐loss data (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:102406

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