Time Series Models

2018-2019
Dit vak wordt in het Engels aangeboden. Omschrijvingen kunnen daardoor mogelijk alleen in het Engels worden weergegeven.

Doel vak

To gain insights in the time series analysis, modelling and prediction
based 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.

Inhoud vak

This course focuses on theory, methodology and computing aspects of time
series 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.

Onderwijsvorm

Main lectures (4 hours); tutorials (2 hours) and computer lab (2 hours)

Toetsvorm

Written exam (0.8), homework and written assignments (0.2).

Algemene informatie

Vakcode E_EORM_TSM
Studiepunten 6 EC
Periode P4
Vakniveau 400
Onderwijstaal Engels
Faculteit School of Business and Economics
Vakcoördinator prof. dr. S.J. Koopman
Examinator prof. dr. S.J. Koopman
Docenten prof. dr. S.J. Koopman

Praktische informatie

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Werkvormen Werkcollege, Hoorcollege
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