Advanced econometrics: Forecasting (5057) |
| Language of instruction : English |
| Credits: 3,0 | | | | | Period: semester 2 (3sp) | | | | | 2nd Chance Exam1: Yes | | | | | Final grade2: Numerical |
| | | Exam contract: not possible |
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Sequentiality
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Advising sequentiality bound on the level of programme components
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Following programme components are advised to also be included in your study programme up till now.
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Econometrics (1543)
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6.0 stptn |
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The student knows the basic principles of estimating a linear regression analysis in a multivariate setting and include formulating and estimating a regression model and interpreting the results. These principles are taught in any basic econometric course.
The use of any software package using econometric tools is required. The course will use R as a software package but prior knowledge of R is not a minimum requirement for the competence.
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In this course, students learn various forecasting techniques in the field of time series analysis. Time series data refer to observations collected over time for one or more economic units. We consider time series at different frequencies (annual, monthly, weekly, daily). A central focus of the course is learning to perform these analyses in practice using R. In addition, students are encouraged to independently address company-related issues and to report in writing on the analyses they have conducted. Techniques covered include autoregressive models, vector autoregression (VAR), cointegration, ARIMA, and (G)ARCH models.
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Lecture ✔
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Response lecture ✔
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Small group session ✔
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Period 2 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 20 % |
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| Additional information | Empirical assignment will count for 20% of the total grade. |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | The written exam counts for 100% of the evaluation. |
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| Compulsory textbooks (bookshop) |
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Stock, J.H. and M.M. Watson (2020). Introduction to Econometrics (fourth edition). Pearson International Edition. |
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| Remarks |
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Additional lecture notes will be made available. |
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Learning outcomes Master of Business Engineering
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- EC
| EC 01: The holder of the degree applies acquired knowledge independently. (Self-direction and entrepreneurial spirit) | - EC
| EC 05: The holder of the degree communicates clearly and correctly in writing and orally, in a business and academic context, if necessary supplemented with visual support. (Communication) | - EC
| EC 08: The holder of the degree shows autonomy in implementing scientific research methods. (Research skills) | - EC
| EC 09: The holder of the degree shows autonomy in analysing, interpreting, evaluating and reporting research results. (Research skills) | - EC
| EC 13: The holder of the degree applies in-depth insights from business science and relevant supporting/related disciplines in the analysis of financial and technical business problems. (Problem-solving capacity) | - EC
| EC 14: The holder of the degree models, designs and evaluates solutions for financial and technical business problems to support decision-making at different levels in a complex context. (Problem-solving capacity) | - EC
| EC 16: The holder of the degree uses IT applications and basic programming skills to translate financial and technical business data into business-relevant information. (Programme-specific competencies) |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
| Offered in | Tolerance3 |
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Exchange Programme Business Economics
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J
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Master handelsingenieur jaar 1 kern verplicht
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J
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1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.15.1, section 3. |
3 Education, Examination and Legal Position Regulations art.16.9, section 2.
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