This course is designed for first-year Statistics Master students or first-year Economics Ph.D. students. The basic methods of modern econometric methods and theory are covered. The intention is that the material will provide a selected survey of the models and methods commonly used in the applied econometrics and statistics. 

The course departs from the standard Gauss-Markov assumptions to include heteroskedasticity, serial correlation, and errors in variables. Advanced topics include large sample theory for estimation and hypothesis testing, generalized least squares, instrumental variables, generalized method of moments (GMM), maximum likelihood estimation (MLE), treatment effects, panel data, bootstrapping, and simulation methods.

单元测验40%+期末考试60%

Students are assumed to have background in linear algebra as well as in probability and mathematical statistics at the level of Olive DJ, Statistical Theory and Inference, 2014, or Hong Y, Probability and Statistics Theory for Economists, 2013.

In addition, students are assumed to have knowledge of the material on the linear regression model covered in Applied Econometrics at the level of Stock JH and Watson MM, Introduction to Econometrics (3rd updata Edition), 2015.

The basic economic theory is help to understand the main idea of models in econometrics, especially in applied econometrics.

We will, however, try to make the course as self-contained as possible by reviewing some of the basic material along the way.

《高级计量经济学》 洪永淼 著   高等教育出版社 ISBN：9787040324242