| Language of instruction : English |
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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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There is no data for this choice.
Change the language, year or choose another item in the dropdown list if it is available.
| Degree programme | | Study hours | Credits | P1 SBU | P1 SP | 2nd Chance Exam1 | Tolerance2 | Final grade3 | |
 | 1st Master of Business Engineering | Compulsory | 162 | 6,0 | 162 | 6,0 | 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 works in a diverse team and displays leadership skills with a multidisciplinary approach to a business problem. (Teamwork) | - 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) |
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| | EC = learning outcomes DC = partial outcomes BC = evaluation criteria |
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The following competences must be acquired to start the course 'Causal inference'. The appolication of:
- Linear Regression with multiple variables: Omitted variable bias, multiple regression model, multicollinearity
- Non-linear regression analysis: Non-linear regression function, log-linear models, interaction terms, non-linear least squares estimator
- Panel data regression analysis: Fixed effects
- Regression with a binary dependent variable: Linear probability model, probit, logit
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The primary goal is to provide students with a solid theoretical and practical basis for (1) the interpretation of empirical economic studies and (2) independently solving economic issues using quantitative causal inference techniques. Each lecture addresses a specific quantitative method, which is subsequently applied to different macro- and micro-economic topics. There are interactive computer sessions that enables students to work with datasets related to the topics discussed throughout the course. At the end of the course, the student should be able to perform empirical analyses independently and to critically evaluate economic studies. The focus is on randomized controlled trials, difference-in-differences, instrumental variables and regression discontinuity.
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Lecture ✔
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Response lecture ✔
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Small group session ✔
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Demonstration ✔
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Exercises ✔
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Group work ✔
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Homework ✔
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Paper ✔
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Presentation ✔
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Period 1 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 20 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Conditions transfer of partial marks within the academic year | only if pass |
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| Oral evaluation during teaching period | 5 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Conditions transfer of partial marks within the academic year | only if pass |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | Participation in all parts of the evaluation is necessary. |
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| Consequences | If the student doesn't participate in all parts of the evaluations, it results in an 'A- unjustified absence' classification. |
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| Recommended reading |
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- [Econometrics by example],[Damodar Gujarati],[Second edition],[Palgrave],[9781137375018],[]
- [Introductory econometrics. A mordern approach],[Jeffrey M. Wooldridge],[5th edition],[South-Western CENGAGE learning],[9781111531041],[]
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| Exchange Programme Business Economics | Optional | 162 | 6,0 | 162 | 6,0 | Yes | Yes | Numerical | |
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The following competences must be acquired to start the course 'Causal inference'. The appolication of:
- Linear Regression with multiple variables: Omitted variable bias, multiple regression model, multicollinearity
- Non-linear regression analysis: Non-linear regression function, log-linear models, interaction terms, non-linear least squares estimator
- Panel data regression analysis: Fixed effects
- Regression with a binary dependent variable: Linear probability model, probit, logit
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The primary goal is to provide students with a solid theoretical and practical basis for (1) the interpretation of empirical economic studies and (2) independently solving economic issues using quantitative causal inference techniques. Each lecture addresses a specific quantitative method, which is subsequently applied to different macro- and micro-economic topics. There are interactive computer sessions that enables students to work with datasets related to the topics discussed throughout the course. At the end of the course, the student should be able to perform empirical analyses independently and to critically evaluate economic studies. The focus is on randomized controlled trials, difference-in-differences, instrumental variables and regression discontinuity.
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Lecture ✔
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Response lecture ✔
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Small group session ✔
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Demonstration ✔
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Exercises ✔
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Group work ✔
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Homework ✔
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Paper ✔
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Presentation ✔
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Period 1 Credits 6,00
| Evaluation method | |
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| Written evaluaton during teaching periode | 20 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Conditions transfer of partial marks within the academic year | only if pass |
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| Oral evaluation during teaching period | 5 % |
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| Transfer of partial marks within the academic year | ✔ |
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| Conditions transfer of partial marks within the academic year | only if pass |
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| Evaluation conditions (participation and/or pass) | ✔ |
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| Conditions | Participation in all parts of the evaluation is necessary. |
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| Consequences | If the student doesn't participate in all parts of the evaluations, it results in an 'A- unjustified absence' classification. |
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| Recommended reading |
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- [Econometrics by example],[Damodar Gujarati],[Second edition],[Palgrave],[9781137375018],[]
- [Introductory econometrics. A mordern approach],[Jeffrey M. Wooldridge],[5th edition],[South-Western CENGAGE learning],[9781111531041],[]
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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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