Topics in Econometrics

Term: 
Fall
Credits: 
2.0
Course Description: 

The goal of the course is to introduce the students to the modern Bayesian econometric analysis of macroeconomic models. Although the course contains a set of theoretical examples and analytical derivations, its focus is mainly on the practical implementation. We will work with reduced-form and structural models and evaluate them against the actual macroeconomic data using computational techniques that are widely employed by modern economists. We will be using MatLab in class and for homework.

Learning Outcomes: 

At the end of the course, students will understand and know how to use Bayesian techniques in order to estimate a macroeconomic model and to evaluate its ability to explain macroeconomic data:

 Convert the model into the format that can be used for likelihood-based Bayesian analysis

 Select prior and apply numerical procedures to compute the posterior distribution of the model

 Use posterior moments for model diagnostics and for comparison between competing models

Assessment: 

 50% homework

 50% final exam

Prerequisites: 

Students will strongly benefit from preliminary knowledge of vector autoregression (VAR) models, dynamic stochastic general equilibrium (DSGE) models and their linear approximation, and likelihood function. These topics will be covered only briefly. Students must be familiar with MatLab

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