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90302 - ADVANCED TIME SERIES ECONOMETRICS

Anno Accademico 2022/2023

                
                        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 (cod. 8408)

                            Risorse didattiche su Virtuale
                        
                                    Orario delle lezioni
                                
dal 08/11/2022 al 12/12/2022

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: 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 (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 Giuseppe Cavaliere