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.)

Instructor(s)

MIT Course Number

14.384

As Taught In

Fall 2013

Level

Graduate

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.

Mikusheva, Anna. 14.384 Time Series Analysis, Fall 2013. (MIT OpenCourseWare: Massachusetts Institute of Technology), http://ocw.mit.edu/courses/economics/14-384-time-series-analysis-fall-2013 (Accessed). License: Creative Commons BY-NC-SA


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