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Activity Number: 144
Type: Contributed
Date/Time: Monday, August 4, 2008 : 10:30 AM to 12:20 PM
Sponsor: Business and Economics Statistics Section
Abstract - #300543
Title: A Robust LOESS Smooth Approach to Problem Bank Identification
Author(s): Randall Kimmel and David Booth*+ and Stephane Booth
Companies: Kent State University and Kent State University and Kent State University
Address: Dept. of Mngmt & IS, Kent, OH, 44242,
Keywords: robust ; loess ; problem bank ; smoothing ; outlier
Abstract:

Continuing bank failures point to the need for early warning problem bank identification models to guide the actions of regulators and investors. Several models have been shown to work well, but most require extensive data preparation/manipulation and custom computer programs to analyze the data, which hinders widespread adoption. In this paper, we show that Robust LOESS (Locally Weighted Scatter Plot Smooth, a type of Local Regression Smoothing), which requires minimal data preparation and can be run in many off the shelf statistical packages such as SAS or SPSS, can be just as effective an early warning system.

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Revised September, 2008