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A Synthetic Penalized Logitboost to Model Mortgage Lending with Imbalanced Data

Jessica Pesantez-Narvaez (), Montserrat Guillen () and Manuela Alcañiz ()
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Jessica Pesantez-Narvaez: Universitat de Barcelona
Montserrat Guillen: Universitat de Barcelona
Manuela Alcañiz: Universitat de Barcelona

Computational Economics, 2021, vol. 57, issue 1, No 13, 309 pages

Abstract: 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.

Keywords: Imbalanced; Boosting; Interpretation; Prediction; Binary (search for similar items in EconPapers)
JEL-codes: C01 C02 C13 C60 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10614-020-10059-5

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