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Inference†in†residuals as an Estimation Method for Earnings Management

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  • Demetris Christodoulou
  • Le Ma
  • Andrey Vasnev

Abstract

The paper tests the assumptions underlying the inference†in†residuals method as an estimation framework for detecting and classifying suspects of earnings management. We derive several systematic biases that are shown to confound inference†in†residuals and, depending on the data, could render the method a futile exercise. This is not a matter of model specification, but a limitation of the statistical method. Also, it is shown that the method of using estimated residuals in a second stage regression on economic determinants of earnings management suffers considerably, especially when residuals are estimated by industry classification in the first stage.

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  • Demetris Christodoulou & Le Ma & Andrey Vasnev, 2018. "Inference†in†residuals as an Estimation Method for Earnings Management," Abacus, Accounting Foundation, University of Sydney, vol. 54(2), pages 154-180, June.
    • Handle: RePEc:bla:abacus:v:54:y:2018:i:2:p:154-180
    DOI: 10.1111/abac.12121
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    3. Andrew B. Jackson, 2022. "Residuals from two‐step research designs," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(4), pages 4345-4358, December.
    4. Majeed, Muhammad Ansar & Yan, Chao & Zhong, Huijie, 2022. "Do firms manipulate earnings after winning public-private partnership bids? Evidence from China," Emerging Markets Review, Elsevier, vol. 51(PB).
    5. Fernando Comiran & Subprasiri Siriviriyakul, 2023. "Detecting overproduction: Evidence from inventory write‐down," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3351-3386, September.
    6. Berrill, Jenny & Campa, Domenico & O'Hagan-Luff, Martha, 2021. "Firm diversification and earnings management strategies: European evidence," International Review of Financial Analysis, Elsevier, vol. 78(C).
    7. Thomas A. Gilliam, 2021. "Detecting Real Activities Manipulation: Beyond Performance Matching," Abacus, Accounting Foundation, University of Sydney, vol. 57(4), pages 619-653, December.
    8. Andrew B. Jackson & Chao Li & Richard D. Morris, 2020. "Earnings Co‐movements and the Informativeness of Earnings," Abacus, Accounting Foundation, University of Sydney, vol. 56(3), pages 295-319, September.
    9. Bedford, Anna & Ma, Le & Ma, Nelson & Vojvoda, Kristina, 2022. "Australian innovation: Patent database construction and first evidence," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    10. Robert Kieschnick & Wenyun Shi, 2021. "Nonstationarity in the relationship between corporate governance and accounting conservatism," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 48(3-4), pages 463-497, March.
    11. Vlad‐Andrei Porumb & Abe De Jong & Carel Huijgen & Teye Marra & Jan Van Dalen, 2021. "The Effect of Auditor Style on Reporting Quality: Evidence from Germany," Abacus, Accounting Foundation, University of Sydney, vol. 57(1), pages 1-26, March.
    12. Guilherme Belloque & Martina K Linnenluecke & Mauricio Marrone & Abhay K Singh & Rui Xue, 2021. "55 years of Abacus: Evolution of Research Streams and Future Research Directions," Abacus, Accounting Foundation, University of Sydney, vol. 57(3), pages 593-618, September.
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    14. Stewart Jones & Nurul Alam, 2019. "A machine learning analysis of citation impact among selected Pacific Basin journals," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(4), pages 2509-2552, December.

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