General Information
Course Code | E_EORM_TSM |
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Credits | 6 EC |
Period | P4 |
Course Level | 400 |
Language of Tuition | English |
Faculty | School of Business and Economics |
Course Coordinator | prof. dr. S.J. Koopman |
Examiner | prof. dr. S.J. Koopman |
Teaching Staff |
prof. dr. S.J. Koopman |
Practical Information
You need to register for this course yourself
Last-minute registration is available for this course.
Teaching Methods | Seminar, Lecture |
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Target audiences
This course is also available as:
Course Objective
To gain insights in the time series analysis, modelling and predictionbased on state space models with a focus on theory, methods and
computations. To obtain experience in empirical modelling and
computational implementation, various computer programs need to be
written.
Course Content
This course focuses on theory, methodology and computing aspects of timeseries analysis, modelling, and prediction based on a general class of
state space models. First, the econometric methodology is explored under
linear Gaussian assumptions. Second, departures from these assumptions
are considered. In particular, we study dynamic models with unobserved
components, signal extraction of dynamic latent variables, parameter
estimation via maximum likelihood, and forecasting. We derive Kalman
filter methods, their nonlinear extensions such as particle filter
methods, and related Monte Carlo simulation methods. All derivations
rely on basic principles in statistics and econometrics. The models and
methods are illustrated for the analysis of macroeconomic, financial and
marketing time series data.