Time Series Analysis
A graduate-level econometrics course from MIT covering the theory and application of time series methods. Topics include univariate stationary and non-stationary models, vector autoregressions, frequency domain methods, estimation and inference in persistent time series, and structural breaks. The course also covers estimation and inference techniques for modern dynamic stochastic general equilibrium (DSGE) models, including simulated method of moments, maximum likelihood, and Bayesian approaches. Empirical applications are drawn mainly from macroeconomics. Materials are provided through MIT OpenCourseWare and typically include lecture notes, problem sets, and readings, free to access with no certificate offered. Suited to students with a background in probability and statistics who want rigorous grounding in econometric time series methods used in macroeconomic research.