Imputation Methods for Incomplete Dependent Variables in Finance

Paul Kofman and Ian Sharpe
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Paul Kofman: University of Melbourne
Ian Sharpe: School of Banking and Finance, University of New South Wales

No 33, Research Paper Series from Quantitative Finance Research Centre, University of Technology, Sydney

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 varaibles 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 treatement of missingness can be important for many financial applications.

Keywords: missing data; em-algorithm; ip-algorithm; multiple imputations; revolving loan characteristics (search for similar items in EconPapers)
JEL-codes: C80 G00 G21 (search for similar items in EconPapers)
Date: 2000-03-01
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Citations: View citations in EconPapers (4)

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