Time series econometrics

Lecturer Binotti Annetta Maria
Semester Fall
ECTS 6 (42h)

Description
This course introduces to the time series methods and practices which are most relevant to the analysis of economic and financial time series. We will
cover univariate and multivariate models of stationary and non-stationary time series in the time domain. The goals of the course are twofold: first to develop a comprehensive set of tools and techniques for analysing various
forms of univariate and multivariate time series, and second to acquire knowledge of recent changes in the methodology of econometric analysis of time series.

Course outline
UNIVARIATE TIME SERIES MODELS
– Moving Average (MA) models
– Autoregressive (AR) models
– Autoregressive Moving Average (ARMA) models
– Choosing a model: the autocorrelation function and the partial autocorrelation function
– Choosing a model: specification tests and model selection criteria
– Stationarity and unit roots
– Testing for unit roots
– Estimation of ARMA models
– Predicting with ARMA models
– Autoregressive conditional heteroskedasticity (ARCH, GARCH and EGARCH).
– Estimation and prediction

MULTIVARIATE TIME SERIES MODELS
– Dynamic models with stationary variables (ADL model, Adaptive expectations, Partial adjustment)
– Models with nonstationary variables
– Spurious regressions
– Cointegration
– Cointegration and error-correction mechanisms
– Vector autoregressive models
– Cointegration: the multivariate case
– Cointegretion in a VAR
– Testing for cointegration