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TIME SERIES AND ECONOMETRICS

TIME SERIES

Program

Updated A.Y. 2018-2019

This course aims at providing a sound knowledge of the basic statistical tools for modelling economic and financial time series.

Univariate Time Series
Stationary time series: Basic concepts. Stationarity, Total and partial autocorrelation, Ergodicity, Linear stationary processes, ARMA models, Outliers, Forecasting.
Nonstationary time series: ARIMA models, The Beveridge-Nelson Trend-Cycle decomposition, Seasonality,
Statistical inference: Estimation, Identification, Diagnostic checking.
Unit roots in economic and financial time series: Deterministic trends vs. random walks, Unit-roots tests, Impulse response function and measures of persistence

Multivariate Time Series
Stationary and Ergodic Multivariate Time Series
Multivariate Wold Representation
Vector Auto-Regressive (VAR) Models
Identification and Estimation of VAR models
Forecasting
Structural VAR Models
Impulse Response Functions
Forecast Error Variance Decompositions
Shocks Identification Using the Choleski Factorization
The Cointegrated VAR
Maximum Likelihood Inference on the Cointegrated VAR
The Common Trends Representation.

Textbook:
Wei (2006) Time Series Analysis: Univariate and Multivariate Methods, second editiom, Addison-Wesley.

Suggested readings:
Brockwell and Davis (2002) Introduction to Time Series and Forecasting, second edition, Springer-Verlag, New York .
Hamilton (1994), Time Series Analysis, Princeton University Press.