MPE_BAAN Bayesian analysis

Faculty of Economics and Administration
Autumn 2021
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
Taught in person.
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský (lecturer)
Mgr. Jakub Chalmovianský (seminar tutor)
Ing. Mgr. Vlastimil Reichel, Ph.D. (assistant)
Guaranteed by
doc. Ing. Daniel Němec, Ph.D.
Department of Economics - Faculty of Economics and Administration
Contact Person: Mgr. Jarmila Šveňhová
Supplier department: Department of Economics - Faculty of Economics and Administration
Wed 14:00–15:50 P106, except Wed 15. 9., except Wed 3. 11.
  • Timetable of Seminar Groups:
MPE_BAAN/01: Wed 10:00–11:50 VT204, except Wed 15. 9., except Wed 3. 11., D. Němec
basic matrix algebra, elementary probability and mathematical statistics, a basic understanding of linear regression or basic econometrics, basic knowledge of Matlab (or similar software)
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 6 fields of study the course is directly associated with, display
Course objectives
The goal of the course is to present the basic elements of Bayesian inference in economic modeling. In economic theory Bayesian methods are central to modeling behavior under uncertainty. Economic agents typically maximize an objective function conditional on their available information, and as more information becomes available they update their decisions using Bayes rule. Bayesian econometrics is based on few simple rules of probability, in particular on the Bayes rule. Our prior views about properties of an economic system (e.g. unknown parameters) are combined with the data information which allows us to update our prior views about unknown parameters.
Methods of Bayesian analysis (Bayesian estimation and computation, model comparison, model prediction) will be explained. The theoretical results will be illustrated by both artificial (to better understand theoretical principles and simulation techniques) and real data analysis (to ilustrate the advantages of Bayesian approach to practical applications of economic models for the purpose of decision-making).
Learning outcomes
At the end of this course, students should be able to:
understand and explain principles of Bayesian analysis of real data;
formulate properly and identify correctly (not only) econometric models regarding specified problem;
understand and evaluate reports in journal articles and other scientific texts using applied Bayesian approach;
interpret (objectively) the results of Bayesian analysis of practical and real (not only economic) issues;
be competent in the use of Matlab and other econometric packages.
  • An Overview of Bayesian Econometrics.
  • The Normal Linear Regression Model with Natural Conjugate Prior (the likelihood function, the prior,the posterior, model comparison, prediction, Monte Carlo Integration).
  • The Normal Linear Regression Model with Other Priors (Gibbs sampler, Markov Chain Monte Carlo diagnostics, Savage-Dickey density ratio).
  • The Nonlinear Regression Model (Metropolis-Hastings algorithm, Gelfand-Dey method).
  • The Linear Regression Model with General Error Covariance Matrix (heteroskedasticity, autocorrelated errors, the Seemingly Unrelated Regressions model).
  • Models with Panel Data (the pooled model, the individual effects models, the random coefficients model, Chib method, efficiency analysis and stochastic frontier model).
  • Introduction to Time Series: State Space Models.
  • Qualitative and Limited Dependent Variable Models (univariate and multionomial Tobit, Probit and Logit models and their extensions).
  • Flexible models (Bayesian non- and semiparametric regression, mixtures of Normal models).
  • Bayesian Model Averaging. Other Models, Methods and Issues.
    required literature
  • KOOP, Gary. Bayesian econometrics. Chichester: Wiley, 2003. xi, 359. ISBN 0470845678. info
  • KOOP, Gary, Dale J. POIRIER and Justin L. TOBIAS. Bayesian econometric methods. 1st ed. Cambridge: Cambridge University Press, 2007. xxi, 357. ISBN 9780521855716. info
    recommended literature
  • LANCASTER, Tony. An introduction to modern Bayesian econometrics. 1st ed. Malden: Blackwell, 2004. xiv, 401. ISBN 9781405117203. info
  • POIRIER, Dale J. Intermediate statistics and econometrics : a comparative approach. Cambridge, Mass.: MIT Press, 1995. xiv, 715. ISBN 0262161494. info
  • BAUWENS, Luc, Michel LUBRANO and Jean-François RICHARD. Bayesian inference in dynamic econometric models. Oxford: Oxford University Press, 1999. xv, 350. ISBN 0198773137. info
  • GEWEKE, John. Contemporary Bayesian econometrics and statistics. Hoboken, N.J.: John Wiley & Sons, 2005. xi, 300. ISBN 0471679321. info
  • ZELLNER, Arnold. An introduction to Bayesian inference in econometrics. New York: John Wiley & Sons, 1971. xv, 431. ISBN 0471169374. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
final (group) project, oral exam
Language of instruction
Follow-Up Courses
Further comments (probably available only in Czech)
Study Materials
The course is taught annually.
General note: Přednášky jsou dostupné online a ze záznamu.
Information on course enrolment limitations: Předmět si nezapisují studenti, kteří absolvovali PMREGR.
The course is also listed under the following terms Autumn 2009, Autumn 2010, Autumn 2011, Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2016, Autumn 2017, Autumn 2018, Autumn 2019, Autumn 2020, Autumn 2022.
  • Enrolment Statistics (Autumn 2021, recent)
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