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\title{Syllabus for AAEC6564 \\ ``Bayesian Econometric Analysis''}
\author{Instructor: Klaus Moeltner}
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Contact Information:\= \\\\%sets the tab stop
office:\>Hutcheson 312\\
phone:\> (540)231-8249\\
e-mail:\> \href{mailto: moeltner@vt.edu}{moeltner@vt.edu}\\
course web site: (TBD)\\\\
Course Information:\\\\
location:\> Davidson 201\\
time:\> TR 11:00am - 12:15pm\\\\
office hours:\> TR, 1-2pm or by appointment\\\\
PREREQUSITES:\\
AAEC 5126 or STAT 5304 or STAT 5444 (or talk to me....)
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\section*{Course Description and Objectives}
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This course is primarily designed for PhD students in Agricultural and Applied Economics (AAEC) and Economics (ECON) beyond their first year. It should also be suitable as an elective for graduate students in Statistics (STAT) with an interest in applied Bayesian modeling.\\
The primary objective of the course is to implement commonly used econometric models in a Bayesian estimation framework. Examples are panel data models, models to detect selection and treatment effects, and models with otherwise limited dependent variables. We will focus on applications where the Bayesian approach brings clear advantages over classical methods, such as estimation under small samples, strong prior information, or model uncertainty. A parallel goal is to instill fluency in Bayesian computational sampling and simulation, most notably Markov-Chain Monte Carlo (MCMC) methods. We will use the software package \texttt{Matlab} for this purpose. Students will program all algorithms ``from scratch'' to become familiar with all model components and details. Throughout the course, we will work with ``real-world'' empirical data sets from various fields of economics.
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\section*{Textbooks}
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I will be primarily using my own lecture notes and sample \texttt{Matlab} code (posted on the web as we move along), but I recommend the following textbooks as useful reference and background sources:\\
\begin{enumerate}
\item \citeasnoun{koopetal2007}: An applied Bayesian text that is light on theory and heavy on intuition, examples, and practice.
\item \citeasnoun{koop2003}. Similar to \citeasnoun{koopetal2007}, but with more details on methodologies and techniques.
\item \citeasnoun{gelmanetal2004}. A comprehensive, all-around reference for Bayesian methods. Nice appendix with explicit forms for many density functions.\\
\end{enumerate}
For those of you interested in the statistical origins and fundamentals of Bayesian analysis, I recommend STAT 5444 ``Bayesian Statistics'', taught by our Statistics Department. Two introductory textbooks used in that course, with focus on statistical foundations of Bayesian methods, are:\\
\begin{enumerate}
\item \citeasnoun{hoff2009}: Very accessible, with detailed derivations.
\item \citeasnoun{berger2006}: Focus on decision theory and Bayes\\
\end{enumerate}
Other Bayesian textbooks I use here and there:\\
\begin{enumerate}
\item \citeasnoun{robert2007}: Decision theory
\item \citeasnoun{albert2009}: Using R for Bayesian analysis
\item \citeasnoun{robertcasella2010}: Using R for Bayesian-style simulation methods\\
\end{enumerate}
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\section{Grading and Other Administrative Issues}
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\subsection*{Grading}
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There will be 6 problem sets that can be completed in team work. However, everybody needs to hand in an individual version. You can earn up to 20 points for each PS for a total of 120 points. The $6^{th}$ problem set has to be handed in on May 8, between 10:05am and 12:05am in lieu of a final exam.
\subsection*{Student conduct}
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The Virginia Tech honor system applies to all graded work in this course. For more information, visit \url{http://www.honorsystem.vt.edu}
\subsection*{Students with disabilities}
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Please let me know if you have a documented disability, so we can provide any accommodations you may need.
\begin{table}[!htp]
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Semester Schedule for AAEC6564, Spring 2020\\
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