A Synthetic Penalized Logitboost to Model Mortgage Lending with Imbalanced Data
Jessica Pesantez-Narvaez (),
Montserrat Guillen () and
Manuela Alcañiz ()
Additional contact information
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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://link.springer.com/10.1007/s10614-020-10059-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:57:y:2021:i:1:d:10.1007_s10614-020-10059-5
Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2
DOI: 10.1007/s10614-020-10059-5
Access Statistics for this article
Computational Economics is currently edited by Hans Amman
More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().