- Docente: Giuseppe Cavaliere
- Crediti formativi: 6
- SSD: SECS-P/05
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
- Corso: Laurea Magistrale in Economics and Econometrics (cod. 5977)
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dal 14/11/2024 al 16/12/2024
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: Stylized facts of financial time series and conditional volatility models: estimation, inference and applications
- Stylized facts of financial data and time series
- Univariate GARCH processes: properties, estimation, diagnostics and inference.
- Applications to Value at Risk.
- Extension to multivariate models of conditional variance.
Part II: Asymptotic and Bootstrap inference in time series
- Introduction to the bootstrap: iid, wild, fixed regressor, moving block, m out of n, permutation, subsampling
- Bootstrapping stationary time series
- Bootstrap inference in multivariate (VAR) models
- Non-stationary time series: bootstrapping unit root and cointegration tests
- Bootstrapping conditional volatility models and the parameter on the boundary problem
Testi/Bibliografia
Lütkepohl H. (2005). New Introduction to Multiple Time Series Analysis. Springer.
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 and written exam (60 minutes).
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 Giuseppe Cavaliere