28 July 2017
This course will provide an introduction to modern Bayesian methods in econometrics.
The first part of the course will present the fundamentals of the Bayesian approach, from the derivation of Bayes' theorem to its practical application to econometric models. It will introduce basic concepts such as prior, posterior and predictive distributions, before presenting essential tools based on simulation methods: Markov chain Monte Carlo methods, including the Gibbs sampler and the Metropolis-Hastings algorithm. Common econometric models students are already familiar with will be revisited from a Bayesian perspective (e.g., linear regression model, binary/discrete variable models).
The second part of the course will dive into more specific and technical topics. It will present some selected econometric models where Bayesian methods are particularly useful, such as latent variable models and random coefficient models (relying on data augmentation methods). It will also discuss some problems that can affect standard simulation methods (e.g., slow convergence, bad mixing), and explain how these problems can be successfully overcome using recent developments in statistics.
Bayesian methods can be applied to any field of economics. The examples and exercises offered during the course will be drawn from various topics, including micro- and macroeconometrics, and finance.
The main goal of this course is to provide students with practical skills to apply Bayesian methods to a specific problem. Therefore, it should be of particular interest for students planning on writing a Master's thesis or preparing for a PhD programme.
EUR 353: Guiding price for EU/EEA citizens. For additional information about the final price, please contact the department that offers the course in question.