Macro-Econometrics - EC887

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2020 to 2021
Canterbury
Spring 7 15 (7.5) checkmark-circle

Overview

The objective of this module is to introduce students to advanced topics in macroeconometrics to enhance independent research. Examples of active topics of research will be provided using examples during the lectures. In order to take part in the module students must have good knowledge of basic time series econometrics and so the module builds upon standard MSc training. Students should also have a working knowledge of MATLAB (hence pre-requisite EC886). Some examples will also be provided in Ox. A basic overview of topics to be covered:
• Introduction
o General review of time series models.
• Filtering theory Part 1
o Linear filters
o Gain and phase
o Band-pass filters
• Filtering theory Part 2
o State-space form
o Kalman filter and Smoother
o Some economic examples
• Estimation
o Maximum likelihood
o Bayesian estimation (Simulation inference: the simulation smoother)
o DSGE example. Time Varying Parameters VAR example
• Markov Switching models
o Discrete filter and smoother
o Economic examples

Details

Contact hours

Total contact hours: 15
Private study hours: 135
Total study hours: 150

Method of assessment

100% coursework:
• Paper Replication Report (five thousand words) (100%)

Indicative reading

• 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.
• Shumway, R.H., and Stoffer, D.S. (2000), Time Series Analysis and Its Applications, Springer-Verlag, New York.
• Cappé, O., Moulines, E., and Rydén, T. (2005). Inference in hidden markov models. Springer Series in Statistics. Springer, New York.
• Fruehwirth-Schnatter, S. (2006). Finite Mixture and Markov Switching Models.Springer Series in Statistics. Springer, New York.
• Hamilton, J. (1994). Time Series Analysis. Princeton University Press.

Learning outcomes

On successfully completing the module students will be able to:
• Read intelligently macro-empirical research (with a proper understanding of the underlying methodology of inference and identification strategy), and
• Conduct empirical research suitable for publication in an economics or econometrics journal.
• Become confident in learning about and understanding novel macro-econometric techniques with a view to implementing them in their own research
• Apply econometrics methods to times-series data
• Handle real data with confidence
• Fully understand the conditions under which particular empirical estimators are appropriate

Notes

  1. Credit level 7. Undergraduate or postgraduate masters level module.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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