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Using Multiple Imputation in the Analysis of Incomplete Observations in Finance

Author

Listed:
  • Paul Kofman
  • Ian G. Sharpe

Abstract

Incomplete observations are a common feature of financial applications that use survey response, annual report, and proprietary banking and security issue and pricing data. Finance researchers use a variety of procedures, including deleting offending observations and imputing ad hoc values, that potentially fail to deliver efficient and unbiased parameter estimates. This article examines the application of a statistical framework, multiple imputation methods, that minimizes incomplete data problems if the missingness satisfies certain criteria. When applied to two financial datasets involving severe data incompleteness, the imputation methods outperform the ad hoc approaches commonly used in the finance literature. , .

Suggested Citation

  • Paul Kofman & Ian G. Sharpe, 2003. "Using Multiple Imputation in the Analysis of Incomplete Observations in Finance," Journal of Financial Econometrics, Oxford University Press, vol. 1(2), pages 216-249.
    • Handle: RePEc:oup:jfinec:v:1:y:2003:i:2:p:216-249
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    Citations

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    Cited by:

    1. Tinashe H. D. Kambadza & Zivanemoyo Chinzara, 2012. "Returns Correlation Structure and Volatility Spillovers Among the Major African Stock Markets," Working Papers 305, Economic Research Southern Africa.
    2. Nikolaus Hautsch & Fuyu Yang, 2014. "Bayesian Stochastic Search for the Best Predictors: Nowcasting GDP Growth," University of East Anglia Applied and Financial Economics Working Paper Series 056, School of Economics, University of East Anglia, Norwich, UK..
    3. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
    4. Cristina Barceló, 2008. "The impact of alternative imputation methods on the measurement of income and wealth: Evidence from the Spanish survey of household finances," Working Papers 0829, Banco de España.
    5. Calzolari, Giorgio & Neri, Laura, 2002. "Imputation of continuous variables missing at random using the method of simulated scores," MPRA Paper 22986, University Library of Munich, Germany, revised 2002.
    6. Abul Shamsuddin & Jae H. Kim, 2010. "Short‐Horizon Return Predictability in International Equity Markets," The Financial Review, Eastern Finance Association, vol. 45(2), pages 469-484, May.
    7. Böhnke, Victoria & Ongena, Steven & Paraschiv, Florentina & Reite, Endre J., 2023. "Back to the roots of internal credit risk models: Does risk explain why banks' risk-weighted asset levels converge over time?," Journal of Banking & Finance, Elsevier, vol. 156(C).
    8. Ahmed Al-Hadi & Grantley Taylor & Grant Richardson, 2022. "Are corruption and corporate tax avoidance in the United States related?," Review of Accounting Studies, Springer, vol. 27(1), pages 344-389, March.
    9. Riandy Laksono & Arianto A. Patunru, 2024. "The dynamics of labor share decline in manufacturing: Evidence from Indonesia," Departmental Working Papers 2024-3, The Australian National University, Arndt-Corden Department of Economics.
    10. Ben Moews, 2023. "On random number generators and practical market efficiency," Papers 2305.17419, arXiv.org, revised Jul 2023.
    11. Lloyd Blenman & Nischala Reddy, 2014. "Leveraged Buyout Activity: A Tale of Developed and Developing Economies," Journal of Financial Management, Markets and Institutions, Società editrice il Mulino, issue 2, pages 157-184, December.
    12. Braun, Reiner & Engel, Nico & Hieber, Peter & Zagst, Rudi, 2011. "The risk appetite of private equity sponsors," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 815-832.
    13. Catherine Norman, 2009. "Rule of Law and the Resource Curse: Abundance Versus Intensity," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 43(2), pages 183-207, June.
    14. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.

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