Applied Time Series Analysis in Banking Risk Management
Introduction to key concepts and applications of time series analysis for bank risk management data-driven decision-making. Analysis, decomposition, segmentation, model selection and estimation, statistical and hypothesis testing, and forecasting and sensitivity testing. Use of actual datasets for applied analysis; revenue forecasting future scenarios; interactive classroom instruction in SAS programming environment.
Data Science and Analytics Interdisciplinary Unit, "DSA652" (2019). Curriculum Proposals. 685.