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MACROECONOMETRICS

DYNAMIC REGRESSION

Program

Updated A.Y. 2017-2018

This module aims at providing a sound knowledge of the basic 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 predictive 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 econometric predictive modelling#
Introduction to Supervised Learning methods and the Validation-Set approach
Best Subset and Stepwise model selection procedures
Regularization (Ridge Regression and Lasso)
Dimension Reduction methods (Principal Component Regression and Partial Least Squares)
Applications to macroeconomic 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.*
James G., Witten D., Hastie T., and R.Tibshirani (2013), An Introduction to Statistical Learning, Springer.#
Hastie T, Tibshirani R., and J. Friedman (2009), Elements of Statistical Learning, Springer.#