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The Influence of Data Imputation Methods on the Classification Efficiency of the Logit Model Used for Forecasting the Bankruptcy of Companies

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  • Dorota Ewa Grochowina

    (Cracow University of Economics)

Abstract

Forecasting the bankruptcy of companies exposes the missing data problem, which applies chiefly to entities having financial problems, who wish to conceal thereby their bad situation. One of the methods of making up incomplete data is imputation. The aim of the paper is to present different data imputation variants and to compare their influence on the classification efficiency of one of the statistical bankruptcy forecasting methods – the logit model. The results have shown that the best approach is to use the median as determined separately for healthy and bankrupt companies.

Suggested Citation

  • Dorota Ewa Grochowina, 2014. "The Influence of Data Imputation Methods on the Classification Efficiency of the Logit Model Used for Forecasting the Bankruptcy of Companies," Acta Universitatis Nicolai Copernici, Ekonomia, Uniwersytet Mikolaja Kopernika, vol. 45(2), pages 187-203.
    • Handle: RePEc:cpn:umkanc:2014:p:187-203
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    More about this item

    Keywords

    bankruptcy; forecasting bankruptcy; logit model; imputation; missing data estimation; model classification efficiency.;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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