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Using Multiple Imputation in the Analysis of Incomplete Observations in Finance

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  • Paul Kofman
  • Ian G. Sharpe
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    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. , .

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    Bibliographic Info

    Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

    Volume (Year): 1 (2003)
    Issue (Month): 2 ()
    Pages: 216-249

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    Handle: RePEc:oup:jfinec:v:1:y:2003:i:2:p:216-249

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    Postal: Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK
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    Cited by:
    1. Tinashe Harry Dumile 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. 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, 05.
    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," Banco de Espa�a Working Papers 0829, Banco de Espa�a.
    5. Giorgio Calzolari & Laura Neri, 2010. "The Method of Simulated Scores for Estimating Multinormal Regression Models with Missing Values," Econometrics Working Papers Archive wp2010_01, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    6. Catherine Norman, 2009. "Rule of Law and the Resource Curse: Abundance Versus Intensity," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 43(2), pages 183-207, June.

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