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General information |
Course unit name: Econometrics I
Course unit code: 366720
Academic year: 2025-2026
Coordinator: Jose Ramon Garcia Sanchis
Department: Department of Econometrics, Statistics and Applied Economics
Credits: 6
Single program: S
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Estimated learning time |
Total number of hours 150 |
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Face-to-face and/or online activities |
60 |
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- Lecture with practical component |
Face-to-face |
45 |
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- Problem-solving class |
Face-to-face |
15 |
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Supervised project |
40 |
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Independent learning |
50 |
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Recommendations |
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Students are recommended to have previously taken the courses in mathematics (Mathematics I and II) and statistics (Statistics for Economics and Business I and II) as this facilitates the proper understanding and application of the course content of Econometrics I. |
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Competences / Learning outcomes to be gained during study |
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Commitment to ethical practice (critical and self-critical skills and attitudes consistent with ethical and deontological principles). |
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Capacity to apply ICTs to professional activities. |
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Ability to use physical and electronic bibliographical resources. |
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Ability to apply the knowledge acquired and analytical skills for solving academic and professional problems. |
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Capacity to prepare, analyse and interpret economic information. |
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Ability to produce critical analyses of economic theories and models. |
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Knowledge and understanding of the nature, sources and uses of economic information and of the appropriate software for processing and analysing economic data. |
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Learning objectives |
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Referring to knowledge Students should acquire the ability to perform professional tasks that focus not so much on the use of econometric tools, but rather on economic issues as seen from global, individual, and sectoral perspectives, in both domestic and international markets. That said, the ability to use econometrics as a research method in the social sciences, and in economics in particular, is essential. |
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Teaching blocks |
Lesson 1. Multiple linear regression models (MLRMs): Introduction
Lesson 2. MLRMs: specification, estimation and validation
Lesson 3. Prediction
Lesson 4. Linear restrictions
Lesson 5. Problems with sample information: influential observations and multicollinearity
Lesson 6. Misspecification off the deterministic assumptions in MRLMs
Lesson 7. Qualitative explanatory variables
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Teaching methods and general organization |
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Given that the learning objectives require substantial practical work, the general teaching methodology combines theoretical instruction with a strong emphasis on practical application. To this end, the following types of activity are undertaken: |
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Official assessment of learning outcomes |
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Continuous assessment consists of two components:
Examination-based assessment Students who are unable to meet the requirements for continuous assessment are entitled to opt for the single mode of assessment. |