Frontiers in Econometrics
Topics in multiple time series analysis
Lecturer: Prof. Dr. Jörg Breitung (University of Bonn)
Date: July 22 – 25, 2013, 9:30 – 15:30 (two small breaks and a 60-minute break at about 12:00)
Venue: University of Leipzig, Wirtschaftswissenschaftliche Fakultät, Grimmaische Str. 12, Fakultäsrechenzentrum, PC-Pool 4
Registration: until June 01, 2013 via email: email@example.com
The objective of the course is to prepare a solid ground for empirical research using advanced econometric techniques from time series and panel data analysis.
The participants are expected to have some knowledge in matrix algebra, probability theory, statistical inference and regression analysis.
Participants can write a term paper including an econometric application. It will also be possible to write the paper in team work of up to 3 persons.
1. Reduced form VAR analysis
1.1 Some useful prerequisites (detrending, cross-correlation)
1.2 Estimation and inference in VAR models
1.3 Lag order selection
1.4 To difference or not to difference
1.5 Causal inference
1.6 Forecasting based on VAR systems
2. Cointegrated systems
2.1 Introduction to the asymptotic analysis of nonstationary time series
2.2 Engle-Granger two-step approach
2.2 Efficient estimation of cointegration relationschips
2.3 Maximum likelihood analysis of cointegrated systems
2.4 Specification and hypothesis testing
3. Structural (identified) VAR models
3.1 Impulse responses based on Cholesky decompositions
3.2 The AB-model of Sims, Gali, Blanchard, Amisano etc.
3.3 Estimation based on ML and IV
3.4 External and internal identification
4. Advanced structural analysis
4.1 Introduction to Bayesian VAR analysis
4.2 SVAR models based on sign restrictions
4.3 Identification through heterogeneity
4.4 Anticipated shocks (news shocks)
5. Dynamic factor models
5.1 The (static) factor model
5.2 Principal Components
5.3 Common effect estimator (CEE)
5.4 Selecting the number of factors
5.5 Dynamic (resp. general) factor models
6.2 Forecasting based on factor models
6.3 Methods of forecast evaluation
6.4 Factor augmented regression models
6.5 Factor augmented vector autoregressions (FAVAR)
7. Panel data analysis of macroeconomic data sets
7.1 The static model
7.2 Specification tests
7.3 Autocorrelation and cross section dependence
7.4 The SUR model
8. Dynamic panel data analysis
8.1 GMM estimator for dynamic panel data models
8.2 System estimator
8.3 Pre-determined and endogenous regressors
8.4 Panel unit root tests
8.5 Panel cointegration
Structural Time Series Analysis
 Enders, W. (2004), Applied Econometric Time Series, 2nd ed., Wiley.
 Harris, R. and R. Sollis (2005), Applied Time Series Modelling and Forecasting, Wiley.
 Lütkepohl, H. (2005), New Introduction to Multiple Time Series Analysis, Berlin: Springer.
 Lütkepohl, H. (2006), Vector Autoregressive Models, in T.C. Mills & K. Patterson (Eds.), Palgrave Handbook of Econometrics, Volume 1, Econometric Theory, Palgrave Macmillan, 477-510.
 Breitung, J., R. Brüggemann and H. Lütkepohl: Structural Vector Autoregressive Modeling and Impulse Responses, in H. Lütkepohl & M. Krätzig (Eds.), Applied Time Series Econometrics, Cambridge: Cambridge University Press.
 Bai, J. and S. Ng (2008b), Large Dimensional Factor Models, Foundations and Trends in Econometrics, 3, 89-163.
 Breitung, J. and I. Choi (2012), Factor Models, forthcoming in: Handbook of Empirical Methods in Macroeconomics, Edward Elgar Publishing.
 Stock and Watson (2011), Dynamic Factor Models, in: M.P. Clements and D.F. Hendry (eds), Oxford Handbook of Economic Forecasting, Chapter 2, Oxford University Press.
Dynamic Panel Data Analysis
 Baltagi, B., The Econometric Analysis of Panel Data, 2005, New York: John Wiley, 3rd ed.
 Breitung, J., and M. H. Pesaran (2008). Unit Roots and Cointegration in Panels. In Matyas, L., and P. Sevestre (Eds.), The Econometrics of Panel Data, 279-322. Kluwer Academic Publishers.
 Wooldridge, J.M. Econometric Analysis of Cross Section and Panel Data, 2002, Cambridge: MIT Press.
 Cameron, A.C. und Trivedi, P.K. Microeconometrics: Methods and Applications, 2005, Cambridge University Press, Chapter V.