Part I: Jesus Bueren 

A ten-hour course introduces students to the analysis, modelling and estimation of stationary time series processes.

Topic 1

Basic Time Series concepts: Recap on difference equations, Stationarity, Ergodicity, ARMA processes.
Hamilton (Chapters 1, 3), Lecture notes.
Topic 2 Maximum Likelihood Estimation: Estimation of ARMA models using MLE. Sta- tistical Inference. Likelihood Ratio test. Model selection criteria.
Hamilton (Chapter 5), Lecture notes.
Topic 3 Multivariate VAR Models : Stationarity, Conditional likelihood and OLS estima- tion, Granger Causality, Impulse responses, error bands, recursive VARs.
Hamilton (Chapter 11), Lecture notes.
 

Exercise classes

There will be 3-4 exercise classes

 

Part II: Barbara Rossi ([email protected])

A 10-hour course that completes an introduction to the analysis, modelling and estimation of time series processes.
Topic 4 Models of Nonstationary Time Series: ADF test, Cointegration, Spurious regres- sion
Hamilton (Chapter 17), Lecture notes.
Topic 5 State-space representation: Kalman filter
Hamilton (Chapter 13), Lecture notes.
Topic 6 Identification of Structural VAR
Vector Autoregressions and Cointegration, Handbook of Econometrics, Vol. 4, Robert
F. Engle and Dan McFadden (editors), North Holland, Lecture notes.
 

Exercise classes

There will be 3-4 exercise classes

 

Part II: Teaching material

• Hamilton, J. H. (1994), Time Series Analysis, Princeton University Press
• Slides notes by the instructor.

 

Final exam and Grading

There will be problems sets graded by TAs in classes (20%) and a final exam (80%).
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