Several examples of time series, collections of data points, measured at successive points in time spaced at uniform time intervals. (Image courtesy of Tomaschwutz. CC BY.)
Instructor(s)
Prof. Anna Mikusheva
MIT Course Number
14.384
As Taught In
Fall 2013
Level
Graduate
Course Description
Course Features
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.
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