90302 - ADVANCED TIME SERIES ECONOMETRICS

Scheda insegnamento

SDGs

L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.

Istruzione di qualità

Anno Accademico 2021/2022

Conoscenze e abilità da conseguire

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.

Contenuti

 

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

 

Testi/Bibliografia

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

Gatarek L., Johansen S. (2015). PDF [https://www.eui.eu/Documents/DepartmentsCentres/Economics/Seminarsevents/Johansen.pdf]

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

Lecture notes provided by the instructors

Metodi didattici

Lectures

Modalità di verifica e valutazione dell'apprendimento

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.

Strumenti a supporto della didattica

A dedicated page on virtuale.unibo.it

Orario di ricevimento

Consulta il sito web di Iliyan Georgiev

Consulta il sito web di Giuseppe Cavaliere