Time series analysis
Master
In Maynard (USA)
Description
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Type
Master
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Location
Maynard (USA)
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Start date
Different dates available
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.
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Course programme
Lectures: 2 sessions / week, 1.5 hours per session
Recitations: 1 session / week 1.5 hours per session
14.382 Econometrics or permission of the instructor.
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.
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 should be empirical.
Hamilton, James D. Time Series Analysis. Princeton University Press, 1994. ISBN: 9780691042893.
The primary text is Hamilton (1994), which is somewhat outdated. A fantastic reference on the current state of the field is the method lectures "What's New in Econometrics-Time Series" delivered by James H. Stock and Mark W. Watson during NBER Summer Institute 2008. 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.
Your feedback is highly valuable. Please, speak up if you have suggestions on how the course can be improved.
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Time series analysis