Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/178203
Title: A Synthetic penalized logitboost to model mortgage lending with imbalanced cata
Author: Pesantez-Narvaez, Jessica
Guillén, Montserrat
Alcañiz, Manuela
Keywords: Anàlisi de regressió
Empirisme
Risc (Economia)
Teoria de l'estimació
Regression analysis
Empiricism
Risk
Estimation theory
Issue Date: 1-Jan-2021
Publisher: Springer Science + Business Media
Abstract: Most classical econometric methods and tree boosting based algorithms tend to increase the prediction error with binary imbalanced data. We propose a synthetic penalized logitboost based on weighting corrections. The procedure (i) improves the prediction performance under the phenomenon in question, (ii) allows interpretability since coefficients can get stabilized in the recursive procedure, and (iii) reduces the risk of overfitting. We consider a mortgage lending case study using publicly available data to illustrate our method. Results show that errors are smaller in many extreme prediction scores, outperforming a number of existing methods. Our interpretations are consistent with results obtained using a classic econometric model.
Note: Versió postprint del document publicat a: https://doi.org/10.1007/s10614-020-10059-5
It is part of: Computational Economics, 2021, vol. 57, num. 1, p. 281-309
URI: http://hdl.handle.net/2445/178203
Related resource: https://doi.org/10.1007/s10614-020-10059-5
ISSN: 0927-7099
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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