TIME-SERIES ANALYSIS AND FORECASTING
The aim of the course is to provide the statistical methods for identifying the dynamics of an economic or financial time-series, for forecasting in absence of structural changes and for modelling the relationship among time-series in a multivariate context.
Passing the exam of Statistics.
I part (24 hours). Stochastic analysis of time-series. Global and partial autocorrelation. AutoRegressive (AR),
Moving Average (MA) and ARMA models. Seasonal models. Box-Jenkins procedure: identification, estimation and
diagnostics. Forecasting theory. Nonstationary time-series. ARIMA models. Seasonal ARIMA models. ADL models.
II part (12 hours). Regime-switching models.
III part (12 hours). Multivariate time series. VAR models. Garnger causality. Cointegration.
IV part (24 hours). Financial time-series. ARCH and GARCH models.
Asymmetric heteroskedastic models. Volatility forecasting. Value-at-Risk estimation.
CRYER CHAN, Time Series analysis with applications in R, Springer
Oral examination. The student has to show to know the main statistical models for the analysis of time series.
Moreover, the student has to analyze the dynamics of a time series using the open source software Gretl or R.