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Imputation Methods for Incomplete Dependent Variables in Finance

Author

Listed:
  • Paul Kofman

    (University of Technology)

  • Ian Sharpe

    (University of New South Wales)

Abstract

Missing observations in dependent variables is a common feature of many financial applications. Standard ad hoc missing value imputation methods invariably fail to deliver efficient and unbiased parameter estimates. A number of recently developed classical and Bayesian iterative methods are evaluated for the treatment of missing dependent variables when the independent variables are completely observed. These methods are compared by simulation to commonly applied alternative missing data methodologies in the finance literature. The methods are then applied to a system of simultaneous equations modelling the maturity, secured status, and pricing of U.S. bank revolving loan contracts. Two of the four dependent variables in this application are characterised by severe missingness. The system of equations approach allows us to also exploit the additional information contained in the interdependencies among these features. The results indicate that proper treatment of missingness can be important for many financial applications.

Suggested Citation

  • Paul Kofman & Ian Sharpe, 2000. "Imputation Methods for Incomplete Dependent Variables in Finance," Econometric Society World Congress 2000 Contributed Papers 0409, Econometric Society.
    • Handle: RePEc:ecm:wc2000:0409
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    References listed on IDEAS

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    Cited by:

    1. Balasubramaniam Meghanadh & Lagesh Aravalath & Bhupesh Joshi & Raghunathan Sathiamoorthy & Manish Kumar, 2019. "Imputation of Missing Values in the Fundamental Data: Using MICE Framework," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 459-475, September.
    2. Andrew J. Patton, 2006. "Estimation of multivariate models for time series of possibly different lengths," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(2), pages 147-173, March.
    3. Patton, Andrew J, 2001. "Estimation of Copula Models for Time Series of Possibly Different Length," University of California at San Diego, Economics Working Paper Series qt3fc1c8hw, Department of Economics, UC San Diego.

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