Optimizing Credit Gaps for Predicting Financial Crises: Modelling Choices and Tradeoffs

39 Pages Posted: 6 Feb 2019 Last revised: 22 Dec 2020

See all articles by Daniel O. Beltran

Daniel O. Beltran

Board of Governors of the Federal Reserve System

Mohammad R. Jahan-Parvar

Board of Governors of the Federal Reserve System

Fiona A. Paine

Board of Governors of the Federal Reserve System

Date Written: January 1, 2019

Abstract

Credit gaps are often used as early warning indicators (EWIs) for financial crises. We evaluate how the performance of credit-gap-based EWIs is influenced by the various modelling choices inherent in their design. For the most common trend-cycle decomposition methods used to recover credit gaps, we find that optimally smoothing the trend enhances out-of-sample prediction. Out-of-sample prediction improves further when we consider a preference for robustness of the credit gap estimates to the arrival of new information, which is important as any EWI should work in real-time. We offer several practical implications.

Keywords: Credit, Credit Gap, Optimization, Predictive Power, Robustness, Trend-Cycle Decomposition

JEL Classification: C22, E39, G28

Suggested Citation

Beltran, Daniel O. and Jahan-Parvar, Mohammad R. and Paine, Fiona, Optimizing Credit Gaps for Predicting Financial Crises: Modelling Choices and Tradeoffs (January 1, 2019). Available at SSRN: https://ssrn.com/abstract=3326228 or http://dx.doi.org/10.2139/ssrn.3326228

Daniel O. Beltran

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

Mohammad R. Jahan-Parvar (Contact Author)

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

HOME PAGE: http://sites.google.com/site/mrjahan/

Fiona Paine

Board of Governors of the Federal Reserve System ( email )

20th Street and Constitution Avenue NW
Washington, DC 20551
United States

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