Advanced Empirical Analysis in Finance
(Master in Quantitative Economics & Finance)
The lectures present the econometric theory behind most of the empirical techniques used in economics and finance. The homeworks that consist of theoretical, computational and empirical exercises ensure that students assimilate the theory and become able to apply it to real data.
Student feedback about the course
“Professor Benjamin Holcblat derives everything that he is teaching. The course has no concept as a black box. The proofs and derivation are done in class which helps you to understand the idea and rigorous reasoning behind it. This class is amazing, because although it is called empirical finance, it actually shows the theory behind empirical finance. I didn't expect this kind of a class, but it sure encouraged me to learn analysis in parallel.”
Outline
Topic 1: Why and When Econometrics Can Work
The "Magic" of Econometrics
Why Econometrics Can Work
When Econometrics Can work
Incorporating Data Dependence
Delta Method
Topic 2: Optimization Estimators (OE) Theory
Definition and Examples of OE
Ordinary Least Squares (OLS) and Nonlinear least squares (NLS)
Maximum Likelihood
Generalized Methods of Moments, and Simulated Method of Moments
Consistency
Uniform Law of Large Numbers
Asymptotic Normality
Topic 3: Elements of Hilbert Space Theory
Inner Product Space
Hilbert Spaces
Projection Theorem
Applications
OLS
Conditional expectation
Omitted Variables: Curse or Blessing?
Topic 4: Hypothesis Testing
Specification Tests vs Goodness-of-fit Tests
The Trinity
Test of overidentifying moment conditions
Topic 5: Simulation-Based Econometrics
Calibration
Simulated method of moments
Indirect Inference*
Topic 5: Elements of (Pseudo)Bayesian Econometrics
Bayesian and PseudoBayesian Inference in a Nutshell
Asymptotic Normality of Pseudo-Posteriors
Consistency of Pseudo-Posteriors
*indicates topics that may be skipped due to time constraint.