Teaching

This is a second-year course in econometrics (focusing on financial econometrics) for MS students in economics. This course will be useful to students who want to get a solid understanding of statistical tools required to analyze and model economic time series data and financial asset prices. The link between new statistical models and implementation is emphasized throughout.

Topics are selected to cover the models which are commonly used in empirical finance literature. Some of these models require econometric analysis which may be highly non-trivial. First we will cover basic and more advanced econometric tools needed for analyzing financial data. These will become our friends in tackling the topics later where we discuss from classical papers of the field to more recent ones in the literature. Recent models include predictive regression, out-of-sample analysis, models with cross-section returns, and more recent topics such as machine learning methods (factor models, LASSO, regression trees, dimension reduction methods, etc.) with applications in empirical asset pricing.

For each topic, we will first develop a good understanding of why certain econometric method works (or does not work) in certain situations, and provide heuristics why sometime new (and more complicated) methods are necessary. Statistical software (R/Python) will then be used to apply these techniques to real data, mostly taken from recent publications from major journals in finance and econometrics. Although we will use statistical software for programming, do not worry about your computer programming language skills. This is not a computer science class and as you shall see later, our use of programming is limited to calculations and simulating data. With that said, the syntax of MATLAB language might take some getting used to at first for those of you who are unfamiliar with it.

The overall goal of this course is to introduce you to the discipline of statistics as a science of understanding and analyzing economic data and not as a branch of mathematics. The class is designed to provide you with the tools needed to answer real-world questions and better understand the process of statistical inference in economics. While a good understanding of these universal statistical tools is its own reward and can find applications in many areas1, our course will mostly discuss examples pertaining to the economics and business world (you are welcome to think about other applications and consult with me if necessary). Additionally, our focus in this course is not an in-depth analysis of a specific field of economics but the tools used in different areas. Therefore, be ready to see examples from different fields of economics (health, education, labor, etc.). Our journey will start from graphical, tabular, and numerical summaries of different types of data and will take us through the topics of probability theory, population and sampling distributions, hypothesis testing, and regression analysis.