This course introduces the principles and techniques for econometric modeling of real data in various business domains. The basic ideas and important techniques for causal inference will also be covered. This course will also elaborate on the modern analysis of high-dimensional business data. The primary topics include multiple testing, methods for causal inference, singular value decomposition, principal component analysis, factor analysis, penalized regression analysis, and classification analysis of high-dimensional data. This course includes hands-on analysis of real-world business cases from domains such as finance, marketing, supply chain management, and manufacturing to apply the econometric and high-dimensional analytical techniques. Students will use R to analyze complex data sets to solve predictive problems in various business functions.

CILO-1: Formulate and test empirical models for causal inference. CILO-2: Conduct dimension reduction. CILO-3: Conduct regularized regression. CILO-4: Conduct classification analysis on high-dimensional business data. CILO-5: Thoroughly describe and interpret analytical results.