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

Autor:	Irimia Diéguez, Ana Isabel, Blanco Oliver, Antonio Jesús, Vázquez Cueto, María José
Přispěvatelé:	Universidad de Sevilla. Departamento de Economía Financiera y Dirección de Operaciones
Jazyk:	angličtina
Rok vydání:	2015
Předmět:	Micro-entities Failure models Financial ratios Logistic regression Non-financial information Classification/regression trees Macroeconomic variables
Zdroj:	idUS. Depósito de Investigación de la Universidad de Sevilla instname
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::66dfa2b02e2ab34f2ef7aab127634b05 Zobrazit plný text záznamu