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Macroeconometrics

Objectives

In this course, students will learn the most important concepts, models, and techniques used in the empirical econometric analysis of macroeconomic data. The focus of the course will be on dynamic models using econometric time series methods. During the course, students will also learn how to use specialized econometric software tools. 


General characterization

Code

2168

Credits

7

Responsible teacher

Luís Catela Nunes

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

n/a

Bibliography

The main material for the course will be available on the course page on Moodle, including papers for specific topics. Recommended readings from the following textbooks will also be indicated on moodle:

Enders, W. (2014), Applied Econometric Time Series, 4th ed., John Wiley & Sons, Inc.

Lütkepohl, H. (2005). New introduction to multiple time series analysis. Springer Science & Business Media.

Hamilton, J.D. (1994), Time Series Analysis, Princeton University Press.

Canova, F. (2007), Methods for Applied Macroeconomic Research, Princeton University Press.  



Teaching method

Students should attend the weekly classes to understand the use of the main time series macroeconometric tools and the theoretical arguments used to derive the main results, and to get acquainted with the interpretation of the results for the selected examples. The use of specialized econometric software will help students understand and apply the methods and models developed during the course. The assignments are important to develop all the learning objectives. 

 


Evaluation method

The final course grade is based on a final exam (60%) and four group assignments (40%). Grades for the assignments will account for the originality, quality, and autonomy of the work done. Detailed instructions are provided on Moodle. 


Subject matter

1. Basic concepts in time-series analysis:  difference equations,  stationarity and ARMA models,  forecasting

2. ARCH and GARCH models

3. VAR models,  reduced form models,  estimation,  structural VAR models with recursive identification,  alternative identification methods

4. Cointegration and VEC models

5. Spectral analysis

6. HP and band-pass filters

7. Bayesian models

8. Models for unobserved components:  state space models,  the Kalman filter,  structural time series models

9. Markov regime switching

10. Threshold models