Bayesian time-series econometrics 2400-ZEWW828
The undoubted advantage of my course are the original analytical and training materials used during classes, the level of detail of which often goes beyond what you can find in any textbook. In addition, all MCMC methods and models discussed during classes will be illustrated with original codes written in R.
The detailed program. The course will consist of 2 modules:
1) Introduction to the Bayesian inference:
a) Bayes' formula, differences between the Bayesian and frequentist paradigms
b) Linear regression in the Bayesian framework
c) AutoRegression (AR) in the Bayesian framework
d) Markov Chain Monte Carlo (MCMC) methods (Gibbs sampling, several variants of the Metropolis-Hastings algorithm and convergence monitoring)
a) Vector AutoRegression (VAR)
b) Structural VAR
c) State-space models
d) Time-Varying Parameters models (TVP)
e) Model described in the article J. Qiu, S. R. Jammalamadaka, and N. Ning (2018), "Multivariate Bayesian structural time series model", Journal of Machine Learning Research, and the dedicated R package "MBSTS"
f) NOTE: there is an option to propose the estimation of any model that participants find useful or interesting, e.g. Stochastic Volatility, Dynamic Factors Models, TVP-VAR, Local Projection, etc.
2) Models discussed (the list of models actually considered during classes may change):
Type of course
Course coordinators
Learning outcomes
Knowledge: The student will understand the Bayesian language; will be able to write code in her/his preferred environment; will learn models used in the modern literature.
Skills: How to use the potential of Bayesian methods in economic/data science modeling; An interested and ambitious student has a chance to become the Bayesian thinker and not (only) a Bayesian user.
Assessment criteria
Preparing empirical report in (max.) 2-person teams, which applies Bayesian methods and uses any programming environment, e.g. R, Python, Matlab, Stata, etc.
To pass the classes the student should submit a report and don’t have more than 3 unexcused absences.
Bibliography
Mainly my detailed slides and/or:
Geweke, J. (2006), Contemporary Bayesian Econometrics and Statistics, Wiley.
Hamilton, J. D. (1994), Time Series Analysis, Princeton University Press.
Koop, G. (2003), Bayesian Econometrics, Wiley
Lancaster, T. (2004), An Introduction to Modern Bayesian Econometrics, Wiley-Blackwell.
Zellner, A. (1971), An Introduction to Bayesian Inference in Econometrics, Wiley.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: