This course seeks to achieve three equally important goals. First, it is intended to expose students to key papers in the structural corporate finance literature. Second, the course is designed to strengthen students’ ability to dissect, digest, and critique academic research. Third, the course is intended to introduce students to the methods used to solve and estimate the parameters of dynamic models.

Please read the preliminary syllabus "FinancialEconometrics_Whited_Syllabus_2024-preliminary" in your Files in CANVAS

Course Materials

Online Material

Online information for this course is being maintained on Dropbox.

Readings

Readings for the course will consist primarily of articles from academic journals, all of which you can find using your online search skills.

The course has two optional textbooks:

• Gouriéroux, Christian, and Alain Monfort, 1996, Simulation-Based Econometric Methods (Oxford University Press, Oxford, United Kingdom).
  You should buy this book if you want to use simulation estimators for your dissertation. It is available from your favorite online textbook supplier.
• Wooldridge, Jeffrey M., 2010, Econometric Analysis of Cross Section and Panel Data, second edition (MIT Press, Cambridge, MA).
  You should just own this book if you want to do any sort of research that involves using data with a cross-sectional dimension.

Course Requirements

Problem Sets and Final

Please organize the answers to the questions in a logical manner, and include computer code when appropriate. All submissions consist of two parts. Part 1 is a pdf file containing the problem answers. You can either write this up in LATEX or write it up by hand and scan it in. For handwritten assignments, please be legible. Word files are not allowed. Organize the write-up in such a way that it is easy to understand. Part 2 is the code. Please compress your answers into a folder and email them to me, but do not use .rar.

For the homework, you must use Julia, Matlab, Python, Gauss, or R. If you want to code in Fortran or C++, go right ahead, but these languages take more time to learn. If you want help, my proficiency is as follows. I can code well in Julia, Fortran, and Gauss. I can read Matlab, Python, C++, and R, in that order of ability, but it is unlikely I will be able to pinpoint any specific errors. All course examples will be in Julia, which is both free and modern.

Class Participation

You are expected to read the papers on the syllabus before coming to class. To make this class work, everyone has to work through every assigned reading before class. The one exception is highly technical econometrics papers. In those cases, you still have to open up the paper and read some of it. You should try to do your best and at least read the introduction, skim the technical sections, and read the conclusion. The papers that absolutely may not be skimmed are marked with an asterisk.

Attendance      20%
Problem Sets   50 %
Final Exam      30 %

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