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Bayesian Econometrics and Decision
Making
Instructor: John
Geweke, University of Iowa
Dates: May 15-19, 2006
Location: American University
Objectives and Scope
Bayesian analysis provides a unified and coherent way of thinking
about decision problems and their solution using data and other
information. The goal of this course is to acquaint the student
in a serious way with this approach and its problem solving potential,
and to this end it has two objectives. The first is to provide
a clear understanding of Bayesian analysis, grounded in the theory
of inference and optimal decision making, which will enable the
student to confidently analyze real problems. The second is to
equip the student with state-of-the-art simulation methods that
can be used to solve these problems.
The course begins with an overview of the entire topic, including
decision problems that motivate Bayesian analysis, principles
of conditioning, updating and combining information, and using
modern computer simulation methods to provide a smooth interface
between data, inference and decision-making. We will work a few
common problems with uncommon solutions to cement the difference
between Bayesian and non-Bayesian inference.
After spending an afternoon on the essential theory of Bayesian
econometrics, the course moves into the computer simulation methods
that revolutionized the entire approach to Bayesian analysis beginning
in the late 1980’s. The course will provide both analytical
understanding of these methods and hands-on experience with how
they work (and when they won’t). We will apply these methods
not just to “estimation” as conventionally defined,
but also to the solution of some realistic decision-making problems.
The course continues by introducing some of the key innovations
that have made Bayesian analysis a flexible and realistic tool
for modeling and decision-making. The emphasis in these innovations
is on methods that are both theoretically sound and also provide
practical approaches of demonstrated superiority in decision making.
The course will also convey some of the “oral wisdom”
of practitioners in developing computer code that is reliable
and runs fast enough to get the job done.
The course will conclude with the presentation of some of the
instructor’s recent experience in forecasting and financial
decision making, and with the opportunity for students to put
forward current problems in research and decision making and see
how they might be addressed by contemporary methods of Bayesian
analysis.
Content and topics
(Reference is to chapters and sections
in the required text.)
Monday, May 15 (morning) Chapter 1
Review and overview of Bayesian methods (1.1-1.6)
Learning session (Selected exercises, ch. 1)
Monday, May 15 (afternoon) Sections 2.1-2.3
Bayesian inference in the linear model (2.1)
Sufficiency and conjugate prior distributions (2.2-2.3)
Tuesday, May 16 (morning) Sections 4.1-4.3
Posterior simulation methods using i.i.d. simulation (4.1-4.2)
Markov chain Monte Carlo methods (4.3)
Tuesday, May 16 (afternoon)
Computer lab session: BACC and the linear model (5.1)
Bayesian decision theory and the combination of information (2.4-2.6)
Wednesday, May 17 (morning)
Computer lab session: Decision making with the linear model (Selected
exercises, 5.1)
Hierarchical priors, latent variables and discrete choice (3.1,
6.1, 6.2)
Thursday, May 18 (morning)
Flexible models (5.4, 6.4.2)
Computer lab session: Using flexible models (Exercises, 5.4 and/or
6.4)
Thursday, May 18 (afternoon)
Tricks of the trade I: Making sure it works (8.1, 8.3)
Tricks of the trade II: Making it work faster (4.4, 4.6)
Friday, May 19 (morning)
Model comparison and Bayesian communication (8.2, 8.4)
Smoothly mixing regressions (Working paper)
Friday, May 19 (afternoon)
Open session: Topics as dictated by student interests and current
concerns in research or decision-making support.
Computer lab session and “hands
on” work
The class will move fairly freely between theory, data and simulation
methods for Bayesian inference. The course will emphasize Matlab
in conjunction with the Bayesian Analysis, Computation and Communications
(BACC) software in much of our work, but prior knowledge of MATLAB
will not be assumed.
Target group and requirements
This course will be of interest to students who have completed
a year of econometrics at the Ph.D. level, and to professional
economists and econometricians who work in support of decision
making in any setting, including mission-oriented government agencies
and private consulting firms. Participants should have some prior
experience using a mathematical applications software package
(e.g. Matlab, Gauss, R, …).
Credits
The course can be taken for three credits or for no credit. To
receive the full three credits, the participant needs to complete
a research paper. Credits can only be obtained by writing the
applied paper. Students can start working on the paper at the
end of the course. The paper is due a number of weeks after the
end of the sessions.
Materials
The course will use the text Contemporary Bayesian Econometrics
and Statistics (Wiley, 2005), written by the instructor. Students
should purchase a copy of this text before the course begins.
One of the sessions will also use the paper “Smoothly Mixing
Regressions” (Journal of Econometrics, forthcoming) by Geweke
and Keane (see link below).
To Order Book use link below or any other source/bookstore:
Ordering information: http://www.wiley.com/WileyCDA/WileyTitle/productCd-0471679321,subjectCd-EC30.html
Paper Download: http://www.biz.uiowa.edu/faculty/jgeweke/papers/SMR/ms.pdf
*Note: This paper and other material will be provided to each
participant at the first meeting.
Costs
Three Credit costs for students. Fixed fee (zero credits) for
Researchers.
Location
American University, Washington, D.C.
Registration
Download the necessary document/s from the main
page and submit to the Economics department, or look at: http://www.american.edu/american/registrar/
About the instructor
John Geweke is a widely-known academic econometrician who has
made important contributions to time series analysis and Bayesian
econometrics, and has applied these methods in many areas of economics.
He is an elected fellow of the Econometric Society and the American
Statistical Association, co-editor of the Journal of Econometrics,
and is a past member of the Committee on National Statistics of
the National Academy of Sciences. He has provided econometric
support for decision making as a consultant to several government
agencies and to firms in the private sector
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