| Language of instruction : English |
| Exam contract: not possible |
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Sequentiality
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No sequentiality
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| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st Master of Business Engineering | Compulsory | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical |  |
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| | | Learning outcomes |
- EC
| The holder of the degree applies acquired knowledge independently. (Self-direction and entrepreneurial spirit) | - EC
| 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
| The holder of the degree shows autonomy in implementing scientific research methods. (Research skills) | - EC
| The holder of the degree shows autonomy in analysing, interpreting, evaluating and reporting research results. (Research skills) | - EC
| 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
| 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
| 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 |
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The student is interested in mathematics and statistics/econometrics in an economic and financial context.
The student knows the basic principles of estimating a linear regression analysis in a multivariate setting.
The students is able to formulate and estimate a regression model. We note that 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.
The student is able to gain economic insights in data interpretation.
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Students learn different techniques in the domain of time series. This course includes two parts: (i) univariate and multivariate time series analysis and (ii) time series analysis of cross-sectional data (panel data). The practical use of STATA is also essential in this course. In addition, students will also analyse and report financial related questions using the empirics.
The content can be summarizes as follows:
*familiarizing themselves with various types of time-series patterns, time series regression, short-term forecasting and panel data analysis;
*Understanding the economic theoretical background, data characteristics, statistical assumptions and characteristics of time series and panel data models;
*first-hand experience of analysing time-series data using computer applications (software STATA);
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Lecture ✔
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Response lecture ✔
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Small group session ✔
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Exercises ✔
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Group work ✔
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Period 1 Credits 3,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 20 % |
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| Written exam | 80 % |
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| Multiple-choice questions | ✔ |
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Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
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| Explanation (English) | For the reset exam will the written exam count for 100% of the total evaluation. |
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| Previously purchased compulsory textbooks |
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Introduction to Econometrics,Stock, J.H. and M.M. Watson,Pearson International Edition |
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| Compulsory course material |
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additional lecture notes. |
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| Exchange Programme Business Economics | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
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The student is interested in mathematics and statistics/econometrics in an economic and financial context.
The student knows the basic principles of estimating a linear regression analysis in a multivariate setting.
The students is able to formulate and estimate a regression model. We note that 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.
The student is able to gain economic insights in data interpretation.
|
|
|
|
Students learn different techniques in the domain of time series. This course includes two parts: (i) univariate and multivariate time series analysis and (ii) time series analysis of cross-sectional data (panel data). The practical use of STATA is also essential in this course. In addition, students will also analyse and report financial related questions using the empirics.
The content can be summarizes as follows:
*familiarizing themselves with various types of time-series patterns, time series regression, short-term forecasting and panel data analysis;
*Understanding the economic theoretical background, data characteristics, statistical assumptions and characteristics of time series and panel data models;
*first-hand experience of analysing time-series data using computer applications (software STATA);
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Lecture ✔
|
|
|
|
Response lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
Period 1 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 20 % |
|
|
|
|
| Written exam | 80 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | For the reset exam will the written exam count for 100% of the total evaluation. |
|
|
|
|
|
| Previously purchased compulsory textbooks |
| |
Introduction to Econometrics,Stock, J.H. and M.M. Watson,Pearson International Edition |
|
|
| Compulsory course material |
| |
additional lecture notes. |
|
|
|
|
|
| 1st Master of Business and Information Systems Engineering | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
| 2nd Master of Business and Information Systems Engineering | Optional | 81 | 3,0 | 81 | 3,0 | Yes | Yes | Numerical | |
|
| | | Learning outcomes |
- EC
| The holder of the degree applies acquired knowledge independently. (Self-direction and entrepreneurial spirit) | - EC
| 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
| The holder of the degree shows autonomy in implementing scientific research methods. (Research skills) | - EC
| The holder of the degree shows autonomy in analysing, interpreting, evaluating and reporting research results. (Research skills) | - EC
| The holder of the degree applies in-depth insights from business science and relevant supporting/related disciplines in the analysis of business and IT problems. (Problem-solving capacity) | - EC
| The holder of the degree models, designs and evaluates solutions for business and IT problems to support decision-making at different levels in a complex context. (Problem-solving capacity) | - EC
| The holder of the degree uses data science and IT to design decision support systems that provide useful insights with which the quality of decisions can be improved. (Programme-specific competencies) |
|
| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
|
|
The student is interested in mathematics and statistics/econometrics in an economic and financial context.
The student knows the basic principles of estimating a linear regression analysis in a multivariate setting.
The students is able to formulate and estimate a regression model. We note that 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.
The student is able to gain economic insights in data interpretation.
|
|
|
|
Students learn different techniques in the domain of time series. This course includes two parts: (i) univariate and multivariate time series analysis and (ii) time series analysis of cross-sectional data (panel data). The practical use of STATA is also essential in this course. In addition, students will also analyse and report financial related questions using the empirics.
The content can be summarizes as follows:
*familiarizing themselves with various types of time-series patterns, time series regression, short-term forecasting and panel data analysis;
*Understanding the economic theoretical background, data characteristics, statistical assumptions and characteristics of time series and panel data models;
*first-hand experience of analysing time-series data using computer applications (software STATA);
|
|
|
|
|
|
|
|
|
Lecture ✔
|
|
|
|
Response lecture ✔
|
|
|
|
Small group session ✔
|
|
|
|
|
|
|
|
Exercises ✔
|
|
|
|
Group work ✔
|
|
|
|
Period 1 Credits 3,00
| Evaluation method | |
|
| Written evaluaton during teaching periode | 20 % |
|
|
|
|
| Written exam | 80 % |
|
|
| Multiple-choice questions | ✔ |
|
|
|
|
|
|
Second examination period
| Evaluation second examination opportunity different from first examination opprt | |
|
| Explanation (English) | For the reset exam will the written exam count for 100% of the total evaluation. |
|
|
|
|
|
| Previously purchased compulsory textbooks |
| |
Introduction to Econometrics,Stock, J.H. and M.M. Watson,Pearson International Edition |
|
|
| Compulsory course material |
| |
additional lecture notes. |
|
|
|
|
|
1 Education, Examination and Legal Position Regulations art.12.2, section 2. |
| 2 Education, Examination and Legal Position Regulations art.16.9, section 2. |
3 Education, Examination and Legal Position Regulations art.15.1, section 3.
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| Legend |
| SBU : course load | SP : ECTS | N : Dutch | E : English |
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