Econometrics 1 develops a deep understanding of Advanced Quantitative Methods, laying a strong foundation for further study in applied economics and finance. The course covers fundamental topics including probability, statistics, advanced regression techniques, and econometric estimation. Students are introduced to a blend of foundational econometric theories and cutting-edge research studies, equipping them with the tools to understand and evaluate real-world economic data.

In addition to mastering econometric methodologies, students develop key research and digital skills by the use of mathematical methods or software to implement advanced econometric models. A strong emphasis is placed on the impact of big data and technological advancements, enabling students to understand both the opportunities and challenges presented by these innovations in the context of econometric analysis. This future-oriented focus ensures students are prepared for the data-driven landscape they will encounter in future studies and professional environments.

The course also enhances students' problem-solving, analytical, and critical thinking skills, fostering independent learning and adaptability. Students will engage in collaborative group work, simulating team-based learning environments like those encountered in professional and hybrid work settings. These experiences help develop their ability to work effectively in interdisciplinary and international teams, boosting their employability in an increasingly globalized economy.

The course includes peer-learning opportunities and exposure to international perspectives, facilitating a think-pair approach and encouraging students to engage with diverse methodologies and datasets. These activities simulate global economic challenges and foster experiential learning by connecting theory with practice, preparing students for further academic research or professional roles that require advanced econometric knowledge.

Synchronous:

10 x 2-hour lectures on campus/online as appropriate for the course content

10 x 2-hour tutorials on campus/online as appropriate for the course content

Asynchronous:

Set of exercises (approximately 20 hours) that will include both theoretical, derivational, and applied type of questions with the corresponding solution guides.

None

None

1. Written assignment, including essay (3 exercises, 11%-12%-12% each); Individual; 1 and a half 5 pages each; 35%; ILOs 1-4, 6.

2. Project output; Group; 2500 words (7 pages excluding tables/figures); 25%; ILOs 3-6.

3. Degree exam: in-person; Individual; 90 minutes; 40%; ILOs 1-3.

It is the default expectation that all courses will offer opportunities for reassessment or deferred assessment. Where it is not possible to offer this in some assessment components, the grade achieved at the first attempt will be counted towards the final course grade, and any exceptions for this course are described below.

[No exceptions]

This course aims to

■ Establish the link between theoretical economic models and empirical econometric estimation.

■ Equip students with advanced tools for testing economic hypotheses, informed by recent developments.

■ Provide a deep understanding of key properties of estimators, including consistency and asymptotic behaviour.

■ Enable students to address practical issues such as parameter identifiability, computation, and statistical inference using economic data.

By the end of this course students will be able to:

1. Critically analyse a wide range of the technical and practical issues associated with econometric models

2. Identify and motivate a series of estimators and estimation methodologies/algorithms, and their optimal use in various empirical scenarios

3. Demonstrate critical knowledge and understanding of technical concepts and research ideas discussed in state-of-the-art applied econometrics articles.

4. Solve specialized applied projects, creatively using a wide range of computer-based packages, preparing for future technological advancements and data challenges

5. Work collaboratively in a group to develop team-working skills and produce a combined piece of coursework.

6. Programme advanced algorithms that allow estimation and statistical inference on real-life scenarios.