90302 - Advanced Time Series Econometrics

Course Unit Page

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2021/2022

Learning outcomes

At the end of the course the student has acquired an advanced and comprehensive knowledge of the main, up-to-date econometric methods for the analysis of economic and financial time series data. In terms of inference techniques, emphasis is given to up-to-date bootstrap methods. In particular, she/he is able: - to analyze critically the application of advanced econometric models to economic time series data; - to implement and make use of proper (asymptotic and bootstrap) inference methods in dynamic environments.

Course contents

Part I: Conditional volatility models: estimation, inference and applications

  1. Univariate GARCH processes: properties, estimation, diagnostics and inference.
  2. Applications to Value at Risk.
  3. Multivariate models of conditional variance: estimation, diagnostics and inference.
  4. Applications to optimal hedging.

Part II: Asymptotic and Bootstrap inference in time series

  1. Introduction to the bootstrap: iid, wild, fixed regressor, moving block, m out of n, permutation, subsampling
  2. Bootstrapping stationary time series
  3. Bootstrap inference in multivariate (VAR) models
  4. Non-stationary time series: bootstrapping unit root and cointegration tests
  5. Bootstrapping conditional volatility models and the parameter on the boundary problem

Readings/Bibliography

Lütkepohl H. (2005). New Introduction to Multiple Time Series Analysis. Springer.

Gatarek L., Johansen S. (2015). PDF

Horowitz J. (2001). The bootstrap. In: Handbook of Econometrics, vol. V.

Lecture notes provided by the instructors

Teaching methods

Lectures

Assessment methods

Take home exam (possibly followed by an oral discussion, on discretion of the course instructors).

Passing numerical grades are intended to match the following qualitative description:

18-23: sufficient
24-27: good
28-30: very good
30 cum laude: excellent.

Teaching tools

A dedicated page on virtuale.unibo.it

Office hours

See the website of Iliyan Georgiev

See the website of Giuseppe Cavaliere