Time Series Analysis

Five brightly colored graphs stacked on top of each other. Each shows a time series process.

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.)


MIT Course Number


As Taught In

Fall 2013



Cite This Course

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.

Anna Mikusheva. 14.384 Time Series Analysis, Fall 2013. (Massachusetts Institute of Technology: MIT OpenCourseWare), http://ocw.mit.edu (Accessed). License: Creative Commons BY-NC-SA

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