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Course Description

The course provides a survey of the theory and application of time series methods in econometrics. Topics covered will include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, models for estimation and inference in persistent time series, and structural breaks. We will cover different methods of estimation and inferences of modern dynamic stochastic general equilibrium models (DSGE): simulated method of moments, maximum likelihood and Bayesian approach. The empirical applications in the course will be drawn primarily from macroeconomics.


The main objective of this course is to develop the skills needed to do empirical research in fields operating with time series data sets. The course aims to provide students with techniques and receipts for estimation and assessment of quality of economic models with time series data. Special attention will be placed on limitations and pitfalls of different methods and their potential fixes. The course will also emphasize recent developments in time series analysis and will present some open questions and areas of ongoing research.


Grades will be determined using the following weights:

Six problem sets 60%
Final exam 40%

The problem sets will emphasize different aspects of the course, including theory and estimation procedures we discuss in class. I strongly believe that the best way to learn the techniques is by doing. Every problem set will include an applied task that may include computer programming. I do not restrict you in your choice of computer language. I also do not require you to write all programs by yourself from scratch. You may use user-written parts of codes you find on the Internet, but I do require that you understand the program you use and properly document it with all needed citations of original sources. Collaboration with other students on problem sets is encouraged, however, the problem sets should be written independently.

If you are an Economics PhD student, your econometrics paper requirement could be fulfilled by turning in a research paper on a topic related to material covered in the class. The paper is due at the end of IAP. The paper should be empirical.

Textbooks and Readings

The primary text is:

Amazon logo Hamilton, James D. Time Series Analysis. Princeton, NJ: Princeton University Press, 1994. ISBN: 9780691042893.

Most of the readings for the later parts of the course are journal articles. The course overviews a large literature, so not all topics are treated in the same depth, and only a few references listed under a topic will be covered. The other papers are additional references for those who wish to study specific topics in greater detail. The lectures will be self-contained.


I am extremely grateful to Jim Stock (Harvard), Rustam Ibragimov (Harvard), Frank Schorfheide (UPenn) and Barbara Rossi (Duke) for their advice and permission to use their course materials.