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Program

Updated A.Y. 2022-2023

ADVANCED TOPICS IN TIME SERIES (DYNAMIC REGRESSION MODULE)

This module aims at providing a sound knowledge of the main results on regression methods for time series data. It is divided in two parts: the first one deals with small-scale dynamic regression models whereas the second one introduces to econometric dynamic modelling with a large number of predictors.

PROGRAM:
Dynamic regression models*
Interdependence
Weak Exogeneity
Granger Causality
Strong Exogeneity
Autoregressive Distributed Lag model
Error (Equilibrium) Correction Model
Common Factors.
Big data in dynamic econometric modelling#
Regularized Linear Models (Ridge Regression and Lasso)
Dimension Reduction methods (Principal Component Regression and Partial Least Squares)
Applications to macroeconomic and financial modelling and forecasting.

REFERENCES:
Harvey A. (1990), The Econometric Analysis of Time Series - 2nd Edition, The MIT Press.*
Hendry D. (1995), Dynamic Econometrics, Oxford University Press.*
P Peña D., and Tsay R.S. (2021) Statistical Learning for Big Dependent Data, Wiley.#