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The course provides a survey of the theory and application of time series methods in econometrics.
0
3
English
English [CC]
FREE
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Description
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.
Course content
- Stationarity, lag operator, ARMA, and covariance structure Unlimited
- Limit theorems, OLS, and HAC Unlimited
- More HAC and intro to spectrum Unlimited
- Spectrum Unlimited
- Spectrum estimation and information criteria Unlimited
- GMM Unlimited
- Weak IV Unlimited
- Bootstrap Unlimited
- Introduction to VARs Unlimited
- VARs Unlimited
- Structural VARs Unlimited
- Factor models Unlimited
- Factor models part 2 Unlimited
- Empirical processes Unlimited
- Unit roots Unlimited
- More non-stationarity Unlimited
- Breaks and cointegration Unlimited
- Cointegration Unlimited
- Filtering, state space models, Kalman filter Unlimited
- State-space models, ML estimation, DSGE models Unlimited
- Intro to Bayes approach, reasons to be Bayesian Unlimited
- MCMC: Metropolis Hastings Algorithm Unlimited
- MCMC: Gibbs sampling Unlimited
Instructor
Massachusetts Institute of Technology
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