syllabus-2019.tex

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\title{Econometrics of Panel Data}\author{Chris Conlon}
\date{Spring 2019}

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\rhead{\footnotesize Applied Econometrics- Fall 2019}
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  E-mail: \texttt{cconlon@stern.nyu.edu} & Web: \href{http://chrisconlon.org}{\tt\bf chrisconlon.org/appliedmetrics}  \\

 Office Hours: Tues/Thurs 11-12 (or by appointment) &  Class Hours: Friday 1:00-4:00 \\


 Office: KMC 7-76  & Class Room: TBA \\
&  \\
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\vspace{10 mm}

\section*{Course Description}
This is a second Ph.D. course in applied econometrics though advanced undergraduates are welcome. The focus is on microeconometrics and panel data. It is a continuation of Prof. Scott's course, though once the basics are covered we will have more opportunity to explore topics related to student intrest.

\begin{description}
\item[Problem Sets:] I have designed the problem sets in R, though you are free to use whichever statistical software you would like. 
\end{description}

\section*{Books}
I will follow two main textbooks.
\begin{itemize}
\item Greene (2017). \textit{Econometric Analysis}. ISBN: 0134461363
\item Tibshirani, Hastie, Friedman (2016), \textit{The Elements of Statistical Learning}. ISBN: 0387848576. Available online at \url{https://web.stanford.edu/~hastie/Papers/ESLII.pdf}.
\end{itemize} 

\section*{Course Policy}
You are expected to attend every lecture and it is expected that you have done the reading BEFORE the class. This is a Ph.D. course which means you will be expected to read a lot on your own.
\subsection*{Grading Policy}
\begin{itemize}
\item 60\% of your grade will be performance on 6 problem sets (10\% each).
\item 30\% of your grade will be performance on the final exam.
\item 10\% of your grade will be participation in class.
\end{itemize}

\subsection*{Academic Dishonesty Policy}
Don't cheat. It is helpful to work with a partner on debugging code, but my expectation is that assignments are 100\% your own work (including computer code).

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\SetDate[01/02/2019]
\week{Week 01} Introduction to Time Series Data
\week{Week 02} Panel Data I : Fixed Effects, Random Effects, Clustering\\
\textit{PS 1 Due}\\
\week{Week 03} Maximum Likelihood and Duration Models\\
\week{Week 04} Discrete Choice and Multinomial Choice\\
\textit{PS 2 Due}
\week{Week 05} Bayesian Methods and MCMC\\
\week{Week 06} Panel Data II : Dynamic Panel,  Causal FE, Empirical Bayes, Hierarchical Models
\textit{PS 3 Due}
\week{Break} \textbf{SPRING BREAK (3/18-3/24)}
\week{Week 07} Treatment Effects and Potential Outcomes\\
\textit{PS 4 Due}
\week{Week 08} Treatment Effects and Potential Outcomes
\week{Week 09} Nonparametric Methods (Kernels, Nearest Neighbors, Bootstrap)\\
\textit{PS 5 Due}\\
\week{Week 10} Nonparametric Methods (Kernels, Nearest Neighbors, Bootstrap)
\week{Week 11} Machine Learning: Model Selection and Regularization (LASSO, RIDGE, PCA)\\
\week{Week 12} Topics based on interest: Duration Models, Dynamic Discrete Choice, Tree Models, Model Averaging Boosting/Bagging, etc.\\
\textit{PS 6 Due}
\week{Week 13} Topics based on interest (continued)





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