Syllabus

I. Course description:

The lecture covers advanced and computational intensive estimation and inference techniques with an emphasis on hands-on exercises using the econometric software Matlab.

  • Cointegration: Engle-Granger Procedure
  • Cointegration: Johansen Procedure
  • Bayesian Estimation: Normal Linear Regression
  • Bayesian Estimation: Normal Nonlinear Regression
  • Bayesian Estimation: State Space Models
  • Monte Carlo and the Bootstrap in Classical Econometrics (if time allows)

 

II. Prerequisities:

The course assumes knowledge of the topics taught in the Econometrics I and Econometrics II.

 

III. Exam:

  • 5 LP
  • written exam (Date: see UniVis)

 

IV. Literature:

Main textbooks (for details see the handouts for each lecture):

  • Hayashi, F. (2000), Econometrics, Princeton University Press.
  • Koop, G. (2003), Bayesian Econometrics, Wiley.
  • Koop, G., D.J. Poirier, J.L. Tobias (2007) Bayesian Econometric Methods, Cambridge University Press.
  • Greene, W.H. (2012), Econometric Analysis, 7th edition, Pearson.