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A Comparison of classification/regression trees and logistic regression in failure models

 

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Opened Access A Comparison of classification/regression trees and logistic regression in failure models
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Author: Irimia Diéguez, Ana Isabel
Blanco Oliver, Antonio Jesús
Vázquez Cueto, María José
Department: Universidad de Sevilla. Departamento de Economía Financiera y Dirección de Operaciones
Date: 2014-10
Published in: Global conference on business, economics, management and tourism (2ª. 2014. Praga)
Document type: Presentation
Abstract: The use of non-parametric statistical methods, the development of models geared towards the homogeneous characteristics of corporate sub-populations, and the introduction of non-financial variables, are three main issues analysed in this paper. This study compares the predictive performance of a non-parametric methodology, namelyClassification/Regression Trees (CART), against traditional logistic regression (LR) by employing a vast set of matched-pair accounts of the smallest enterprises, known as micro-entities,from the United Kingdom for the period 1999 to 2008 that includes financial, non-financial, and macroeconomic variables. Our findings show that CART outperforms the standard approach in the literature, LR.
Cite: Irimia Diéguez, A.I., Blanco Oliver, A.J. y Vázquez Cueto, M.J. (2014). A Comparison of classification/regression trees and logistic regression in failure models. En Global conference on business, economics, management and tourism (2ª. 2014. Praga)
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URI: https://hdl.handle.net/11441/81067

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