2018
DOI: 10.1080/1331677x.2017.1421991
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Estimating accruals models in Europe: industry-based approaches versus a data-driven approach

Abstract: Accruals models have been estimated using a variety of approaches, but the industry-based cross-sectional approach currently seems to be the standard method. This estimation approach cannot be easily used in the vast majority of European countries where several industry groups do not have sufficient yearly observations. Using data from France, Germany, Italy and the UK, we artificially induce earnings manipulations to investigate how the ability to detect those manipulations through accruals models is affected… Show more

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Cited by 6 publications
(2 citation statements)
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References 46 publications
(63 reference statements)
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“…Secondly, some models require a fairly large number of observations and variables, which make their estimates much more restrictive (Degeorge et al, 2013). Thirdly, the most popular estimation approach is the cross-sectional by year and industry approach and the SIC codes at two-digit level are the most common industry classification used (Di Narzo et al, 2018). However, this approach is impossible due to the lack of sufficient observations in each two-digit SIC code (Ecker et al, 2013).…”
Section: Internal Control Measurementioning
confidence: 99%
“…Secondly, some models require a fairly large number of observations and variables, which make their estimates much more restrictive (Degeorge et al, 2013). Thirdly, the most popular estimation approach is the cross-sectional by year and industry approach and the SIC codes at two-digit level are the most common industry classification used (Di Narzo et al, 2018). However, this approach is impossible due to the lack of sufficient observations in each two-digit SIC code (Ecker et al, 2013).…”
Section: Internal Control Measurementioning
confidence: 99%
“…The authors consider their conclusions to be robust with respect to the latest innovations in proxies for the topic of earnings management. Di Narzo et al (2018) in their study used data from France, Germany, Italy and the UK to investigate how the ability to detect earnings manipulations through accruals models are affected by the use of different industry classifications. Their analyses showed that enlarging the industry classification reduces the probability of discovering earnings manipulations.…”
Section: Literature Reviewmentioning
confidence: 99%