Undergraduate Courses


In this course, we will introduce empirical network analysis in Economics. Facing the rapid growth of network studies in the economic research, it becomes an attractive and must-know subject for economic undergraduates, particularly when network data are widely available in this big data era. In the first half of this course, we will learn how to describe network data and incorporate network information into the regression analysis. Next, we will discuss how to empirically model peer (spillover) effects in networks and network formation. In the second half of the course, we will cover techniques to obtain and process data for network analysis, as well as machine learning methods commonly used in this area. Throughout this course, students will learn to use two types of software—R and Python—to collect, arrange, and analyze network data. Students will also learn network visualization software such as NetDraw and Gephi to facilitate presentation of analysis results. Students should have knowledge of basic statistics and regression analysis, and are advised to take ECON2121 (or equivalent) and ECON3121 (or equivalent) before taking this course.


1st Term
2nd Term
ECON4130 Teacher: Dr. CHOW Yan Chi, Vinci [Course Outline]
Prof. HSIEH Chih Sheng