Writing a project is an important and necessary task for every economist. What are the skills you need to do an independent, original research project? You will be guided through the process of developing and refining a project proposal exactly as you might produce in further study or in future employments l. Based on your initial ideas, you will be matched with a supervisor to help you refine your research question and identify the relevant literature, research methods and actions you will need to answer it. Lectures and seminars will introduce the main empirical strategies for causal inference. You will learn about identification based on observables, randomized control trials, difference-in-differences, instrumental variables, and regression discontinuity design. You will also use econometric software in the analysis of data and estimations. By the end of the module, you will be able to analyse datasets using these methods and present a refined dissertation proposal that will appropriately apply one or more of these methods to your question. Moreover, you will be encouraged to think forward to how the year at Aix Marseille might expand on the skills you have learned at Kent, considering how Economics expertise function within a whole host of employments both nationally and Internationally.
Lecture 16, PC Labs 13, Supervision 3
The module is compulsory for the following courses
MSc Economics & Data Science
This module is not available as an optional module
Presentation. Assessment Details: Individual Presentation 15-20 slides worth 20%.
Test. Assessment Details: Take Home Test 1 hour worth 30%.
Extended Writing. Assessment Details: Project Proposal 1500 words worth 50%.
Reassessment Method: Single instrument. 100% written assessment (take home test, 2 hours in a 24 hour window)
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices. The most up to date reading list for each module can be found on the university's reading list pages.
On successfully completing the module, students will be able to:
Draw on a systematic understanding and critical awareness of relevant literature and research methods for causal inference in order to identify your chosen project topic and how you would use Big Data to address it.
Critically evaluate rigorous tools of theoretical analysis and empirical modelling in order to select relevant theoretical and/or empirical methods from a wide range of complex techniques for your project related to Big Data analysis.
Reflect on current debates and practices in economics, including causal inference and Big Data, and present original ideas for applying these.
Deal systematically with complex economic relationships, reflect on the state of art models on these data and generate sharp predictions and convincing arguments about the relationships in question.
Communicate effectively and clearly with specialist and non-specialist audiences work by a variety of methods.
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