## PMREGR Regression analysis

Autumn 2008
Extent and Intensity
2/2. 5 credit(s). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Daniel Němec, Ph.D. (lecturer)
doc. Ing. Daniel Němec, Ph.D. (seminar tutor)
Guaranteed by
prof. Ing. Osvald Vašíček, CSc.
Department of Economics - Faculty of Economics and Administration
Contact Person: Lydie Pravdová
Timetable
Tue 14:35–16:15 P201
• Timetable of Seminar Groups:
PMREGR/1: Wed 14:35–16:15 VT203, D. Němec
Prerequisites
basic matrix algebra, elementary probability and mathematical statistics, a basic understanding of linear regression or basic econometrics
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
Course objectives
Regression analysis (PMREGR) - in the future Bayesian analysis or Bayesian econometrics. 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).
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.
Syllabus
• 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).
• Models with Panel Data
• Introduction to Time Series: State Space Models.
• Qualitative and Limited Dependent Variable Models
• Bayesian Model Averaging.
• Other Models, Methods and Issues.
Literature
• Koop, G.: Bayesian Econometrics. Wiley, Chichester 2003. ISBN 0-470-84567
• Lancaster, T.: An Introduction to Modern Bayesian Econometrics. Blackwell Publishing, Malden 2004. ISBN 1-4051-1719-2
• Koop, G., Poirier, D.J., Tobias, J.L.: Bayesian Econometrics Methods. Cambridge University Press 2007. ISBN 0-521-67173-6
• Geweke, J.: Contemporary Bayesian Econometrics and Statistics. Wiley, New Jersey 2005. ISBN 0-0237-4530-4
• LeSage, James P.: Applied Econometrics using MATLAB. 1999. Dostupné na http://www.spatial-econometrics.com
• Ghosh, Jayanta K., Delampady, M., Samanta T.: An Introduction to Bayesian Analysis – Theory and Methods. Springer, New York 2006. ISBN 0-387-40084-2.
• Bolstad, William M.: Introduction to Bayesian Statistics. Wiley, New Jersey 2004. ISBN 0-471-27020-2
• Greene, William H.: Econometric Analysis. 5th edition, Prentice Hall, New Jersey 2003. ISBN 0-13-066189-9
• Poirier, D.J.: Intermediate statistics and econometrics: a comparative approach. MIT Press, Cambridge 1995. ISBN 0-262-16149-4
Assessment methods
lectures, class discussion, computer labs practices, drills; final (group) project, oral exam
Language of instruction
Czech
Further comments (probably available only in Czech)
The course is taught annually.
The course is also listed under the following terms Autumn 2007.
• Enrolment Statistics (recent)