MPE_BAAN Bayesian analysis

Faculty of Economics and Administration
Autumn 2024
Extent and Intensity
2/2/0. 10 credit(s). Type of Completion: zk (examination).
In-person direct teaching
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Mgr. Jakub Chalmovianský, Ph.D. (assistant)
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
Timetable
Wed 14:00–15:50 P106, except Wed 18. 9., except Wed 6. 11.
  • Timetable of Seminar Groups:
MPE_BAAN/01: Wed 10:00–11:50 VT204, except Wed 18. 9., except Wed 6. 11., D. Němec
Prerequisites
basic matrix algebra, elementary probability and mathematical statistics, a basic understanding of linear regression or basic econometrics, basic knowledge of R, 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 alternative programming languages.
Syllabus
  • An overview of Bayesian approach in econometrics and statistics
  • Bayes' rule (building a Bayesian model for events and random variables)
  • The Beta-Binomial Bayesian model
  • Balance and sequentiality in Bayesian analysis
  • Conjugate families
  • Approximating the Posterior
  • Markov Chain Monte Carlo methods
  • Posterior inference and prediction
  • Simple normal regression
  • Evaluating regression models
  • Extending the Normal regression model
  • Poisson and Negative Binomial Regression
  • Logistic Regression
  • Naive Bayes Classification
  • Hierarchical Bayesian Models
Literature
    required literature
  • JOHNSON, Alicia A., Miles Q. OTT and Mine DOGUCU. Bayes rules! : an introduction to applied Bayesian modeling. First edition. Boca Raton: CRC Press/Taylor & Francis Group, 2022, xxi, 521. ISBN 9780367255398. info
  • KOOP, Gary. Bayesian econometrics. Chichester: Wiley, 2003, xi, 359. ISBN 0470845678. info
  • LAMBERT, Ben. A student's guide to Bayesian statistics. First published. Los Angeles: Sage, 2018, xx, 498. ISBN 9781473916364. info
    recommended literature
  • MCELREATH, Richard. Statistical rethinking : a Bayesian course with examples in R and Stan. Second edition. Boca Raton: CRC Press/Taylor & Francis Group, 2020, xvii, 593. ISBN 9780367139919. info
  • KRUSCHKE, John K. Doing Bayesian data analysis : a tutorial with R, JAGS and Stan. Edition 2. Amsterdam: Elsevier, 2015, xii, 759. ISBN 9780124058880. info
Teaching methods
lectures, class discussion, computer labs practices, drills
Assessment methods
seminar activity and two semestral homework assignments (50% of the final grade), a final (group) project and an oral examination in the form of a project defence (50% of the final grade); details of the course completion for students going abroad are contained in the Organisational guidelines (see study materials in IS)
Language of instruction
Czech
Follow-Up Courses
Further comments (probably available only in Czech)
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.
Teacher's information
Any copying, recording or leaking tests, use of unauthorized tools, aids and communication devices, or other disruptions of objectivity of exams (credit tests) will be considered non-compliance with the conditions for course completion as well as a severe violation of the study rules. Consequently, the teacher will finish the exam (credit test) by awarding grade "F" in the Information System, and the Dean will initiate disciplinary proceedings that may result in study termination.
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 2021, Autumn 2022, Autumn 2023.
  • Enrolment Statistics (recent)
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