Forecasting Bankruptcy More Frequently: Information Update via High Frequency Data
회귀모형에서 혼합주기자료를 이용한 정보 업데이트 방법에 관한 이론 및 실증연구
- Myungwon Kim
- 사회과학대학 경제학부
- Issue Date
- 서울대학교 대학원
- mixed frequency data; information update; outdated data; omitted variable bias; default forecasting; credit risk monitoring
- 학위논문 (박사)-- 서울대학교 대학원 : 경제학부 경제학 전공, 2016. 2. 류근관.
- This paper considers the econometric problems arising from using outdated data in a regression model in which the independent variable is observed less frequently than the dependent variable. Specifically, OLS estimates may suffer from a form of omitted variable bias if outdated data is correlated with information during the time no observation takes place. We claim that using data correlated with the independent variable but with a shorter observation period to update the independent variable can eliminate the bias, as well as reducing uncertainty in estimating the dependent variable. We test the theory with an empirical model of bankruptcy forecast for medium sized firms. We present a more accurate default forecast model that updates the average change in firms’ financial standing with monthly business cycle information. Financial institutions may use the monthly estimates to monitor losses on their loan portfolios more accurately and more frequently.