Master of Science in Economics

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.#

Updated A.Y. 2014-2015

Dynamic regression models

Programme

Interdependence
Weak Exogeneity
Granger Causality
Strong Exogeneity
Autoregressive Distributed Lag model
Error (Equilibrium) Correction Model
Common Factors
Structural Breaks
Spurious Regression
Elements of Inference for I(1) Processes
Cointegration (bivariate case)
Inference on Cointegration: Single Equation Methods.

LIST of REFERENCES:

Banerjee, Dolado, Galbraith and Hendry (1993) Co-Integration, Error-Correction and the Econometric Analysis of Non-Stationary Data, Oxford University Press.
Heij, De Boer, Franses, Kloek, and Van Dijk (2004), Econometric Methods with Applications in Business and Economics, Oxford University Press.
Harvey (1990) The Econometric Analysis of Time Series - 2nd Edition, The MIT Press.
Hendry (1995), Dynamic Econometrics, Oxford University Press.
Hansen, B. H. (2001), "The new econometrics of structural change: dating breaks in US labor productivity", Journal of Economic Perspectives, 15, 117-128.

Introduction to state space modelling

Programme

1. Introduction. The state space representation and its role in macroeconometrics. Signal extraction and forecasting under squared loss.

2. Unobserved components models for economic time series. Models for the trend component. Cyclical components. Seasonality and Calendar components. Outliers and structural breaks.

3. State space models and their statistical treatment. The Kalman filter Maximum likelihood estimation. Smoothing filters. Forecasting. Diagnostics.

4. Applications.

Course material

The course is mostly based on

Harvey A.C., Time Series Models, 2nd Edition, Harvester Wheatsheaf, 1993, Chapters 4 and 5.

Harvey A.C., Forecasting, Structural Time Series and the Kalman Filter, Cambridge University Press, Cambridge, UK, 1989. Chapters 2-3.

Proietti, T. (2002), Forecasting with Structural Time Series Models, in Clements, M.P. and D. F. Hendry (eds.), A Companion to Economic Forecasting, Blackwell Publishers, Oxford

Readings list

Durbin, J., and Koopman, S.J. (2001), Time Series Analysis by State Space Methods, Oxford University Press, Oxford, UK.

Harvey, A.C. (1989), Forecasting, Structural Time Series and the Kalman Filter, Cambridge University Press, Cambridge, UK.

West, M., and Harrison, J. (1997), Bayesian Forecasting and Dynamic Models, 2nd ed., Springer-Verlag, New York.

Kim, C.J., and Nelson, C. R. (1999), State-Space Models with Regime-Switching. Cambridge MA: MIT Press.

Fahrmeir, L., and Tutz G. (2001), Multivariate Statistical Modelling Based on Generalized Linear Models, Springer-Verlag, New York.

Shumway, R.H., and Stoffer, D.S. (2000), Time Series Analysis and Its Applications, Springer-Verlag, New York.

Harvey, A.C., Koopman S.J., and Shephard, N. (eds.) (2004), State Space and Unobserved Component Models, Cambridge University Press, Cambridge, UK.

Harvey A.C. and Proietti, T. (eds.) (2005), Readings in Unobserved Components Models, Oxford University Press, Oxford, UK.

Applications

Harvey, A.C., and Jaeger, A. (1993). Detrending, stylized facts and the business cycle. Journal of Applied Econometrics, 8, 231-247.

Hodrick R.J., and Prescott, E.C (1997). Postwar U.S. Business Cycles: an Empirical Investigation, Journal of Money, Credit and Banking, 29, 1-16.

Watson, M.W. (1986). Univariate detrending methods with stochastic trends. Journal of Monetary Economics, 18, 49-75.

Proietti, T. (2008) Structural Time Series Models for Business Cycle Analysis. To appear in the Handbook of Econometrics: Vol. 2, Applied Econometrics, Part 3.4., ed. T. Mills and K. Patterson, Palgrave, London, forthcoming, 2008.