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Improving Forecast of Binary Rare Events Data: A GAM-Based Approach

Calabrese, Raffaella and Osmetti, Silvia Angela (2015) 'Improving Forecast of Binary Rare Events Data: A GAM-Based Approach.' Journal of Forecasting, 34 (3). pp. 230-239. ISSN 0277-6693

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Abstract

This paper develops a method for modelling binary response data in a regression model with highly unbalanced class sizes. When the class sizes are highly unbalanced and the minority class represents a rare event, conventional regression analysis, i.e. logistic regression models, could underestimate the probability of the rare event. To overcome this drawback, we introduce a flexible skewed link function based on the quantile function of the generalized extreme value (GEV) distribution in a generalized additive model (GAM). The proposed model is known as generalized extreme value additive (GEVA) regression model, and a modified version of the local scoring algorithm is suggested to estimate it. We apply the proposed model to a dataset on Italian small and medium enterprises (SMEs) to estimate the default probability of SMEs. Our proposal performs better than the logistic (linear or additive) model in terms of predictive accuracy.

Item Type: Article
Uncontrolled Keywords: generalized additive model;rare event;generalised extreme value distribution;local scoring algorithm
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculty of Social Sciences > Essex Business School
Faculty of Social Sciences > Essex Business School > Essex Finance Centre
Depositing User: Jim Jamieson
Date Deposited: 11 May 2015 11:09
Last Modified: 13 Nov 2015 16:33
URI: http://repository.essex.ac.uk/id/eprint/13693

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