This is the first course in econometric theory for Ph.D. students; well prepared Master students are also welcome to take this course. This course requires knowledge of probability theory, multivariate statistics, and linear (matrix) algebra. Some econometrics knowledge at master level is a plus but not required. In this course, I will follow my own lecture notes and cover the least-squares theory and quasi-maximum likelihood theory. Unlike most econometrics textbooks that are organized according to models, my notes are arranged by theories (methods), with applications to various models. Some textbooks (R2 and R3 below) provide more thorough treatment of these topics. By introducing econometric theory in this way, I hope students will learn how an econometric method is derived and why it works. In addition, commonly used computational methods in econometrics, such as Monte Carlo simulation and bootstrap, will also be introduced.

The lectures will be in English; classroom discussion may be in Mandrin. To conduct simulations and bootstraps, students are required to learn at least one programming language, such as R (a free software) or Matlab. A senior student will introduce basic programming in R in the beginning lectures; some basic materials about R installation and introduction can be found in the class website (see below).