ECON 371
Time Series Econometrics and Empirical Methods for Macro Fall 2018 Division II; Quantative/Formal Reasoning;

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Econometric methods in many fields including macro and monetary economics, finance and international growth and development, as well as numerous fields beyond economics, have evolved a distinct set of techniques which are designed to meet the practical challenges posed by the typical empirical questions and available time series data of these fields. The course will begin with an introductory review of concepts of estimation and inference for large data samples in the context of the challenges of multivariate endogeneous systems, and will then focus on associated methods for analysis of short dynamics such as vector autoregressive techniques and methods for analysis of long run dynamics such as cointegration techniques. Students will be introduced to concepts and techniques analytically, but also by intuition, learning by doing, and by computer simulation and illustration. The course is particularly well suited for economics majors wishing to explore advanced empirical methods, or for statistics, mathematics or computer science majors wishing to learn more about the ways in which the subject of their majors interacts with the fields of economics. The method of evaluation will include a term paper. ECON 252 and either STATS 346 or ECON 255 are formal prerequisites, although for students with exceptionally strong math/stats backgrounds these can be waived subject to instructor permission. Credit may not be earned for both ECON 371 and ECON 356.
The Class: Type: seminar
Limit: 19
Expected: 19
Class#: 1184
Requirements/Evaluation: term paper and regular homework assignments
Extra Info: may not be taken on a pass/fail basis
Prerequisites: ECON 252 and either ECON 255 or STATS 346
Enrollment Preference: students wishing to write an honors thesis, and students with strong MATH/STAT/CSCI backgrounds
Distributions: Division II; Quantative/Formal Reasoning;
Distribution Notes: QFR: Uses quantitative/formal reasoning intensively in the form of mathematical and statistical arguments, as well as computer programming.

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