Courses Bachelor Display 2023-2024
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| Course title | Introduction to Software in Econometrics | |||||||||||||||||||||||||||||||||||||||
| Course code | EBS2072 | |||||||||||||||||||||||||||||||||||||||
| ECTS credits | 4,0 | |||||||||||||||||||||||||||||||||||||||
| Assessment | Whole/Half Grades | |||||||||||||||||||||||||||||||||||||||
| Period |
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| Level | Intermediate/Advanced | |||||||||||||||||||||||||||||||||||||||
| Coordinator |
Nalan Bastürk For more information: n.basturk@maastrichtuniversity.nl |
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| Language of instruction | English | |||||||||||||||||||||||||||||||||||||||
| Goals |
1. The student will learn to model simple econometric problems in the Bayesian framework.
2. The student will learn how to implement simulation-based Bayesian inference procedures for standard econometric models in the statistical programming software R and how to interpret estimation results. 3. The student will learn how to assess the appropriateness and accuracy of different simulation methods in different examples. |
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| Description |
This course covers advanced econometric techniques for modelling financial time series. Topics that are covered include ARIMA, VAR, volatility models and high dimensional modeling. Empirical applications will provide students with practical experience in analysing financial time series.
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| Literature |
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| Prerequisites |
Econometric Methods 1 (EBC2111)
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| Keywords |
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| Teaching methods (indicative; course manual is definitive) | PBL / Presentation / Lecture / Assignment / Papers / Groupwork / Skills | |||||||||||||||||||||||||||||||||||||||
| Assessment methods (indicative; course manual is definitive) | Final Paper / Attendance / Written Exam / Assignment | |||||||||||||||||||||||||||||||||||||||
| Evaluation in previous academic year | For the complete evaluation of this course please click "here" | |||||||||||||||||||||||||||||||||||||||
| This course belongs to the following programmes / specialisations |
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