Studying at York
Econometric Analysis - ECO00032I
- Department: Economics and Related Studies
- Credit value: 20 credits
- Credit level: I
- Academic year of delivery: 2023-24
- See module specification for other years: 2024-25
Module summary
The module will introduce key concepts and techniques available to estimate models in economics, test relationships between economic variables and make predictions.
The emphasis will be on applying econometrics to real-world problems, interpreting the results of econometric models and thinking critically about their limitations.
The module will improve your understanding of empirical academic papers and give you the necessary skills to evaluate their research. You will also develop your own data analysis skills with the use of software packages such as Stata
Related modules
Pre-requisite modules
Co-requisite modules
- None
Prohibited combinations
- None
Additional information
Prerequisite modules: Probability and Statistics OR Quantitative Methods
Econometrics has a strong emphasis on applications and only requires a knowledge of linear algebra and basic probability and statistics acquired in semesters 1 and 2 with Maths for Economists and Probability and Statistics or Quantitative methods. It also builds on the knowledge learned in Data, Evidence and Policy, however this module is not a requirement.
Students who would like a more theoretical approach are encouraged to also take the Semester 4 optional module Econometric Theory.
Econometrics will provide the necessary skills to understand and evaluate empirical economic papers used in most applied modules in Semesters 5 and 6 such as Contemporary Economic Issues and Analysis, Political Economics, Health Economics, Labour Economics or Economics of Social Policy. These skills are also transferable to other social sciences disciplines such as Politics or quantitative History.
Students choosing to write a Dissertation will have the necessary toolkit to perform an econometric analysis, interpret and think critically about their results.
Module will run
| Occurrence | Teaching period |
|---|---|
| A | Semester 2 2023-24 |
Module aims
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To develop students’ knowledge of econometric techniques and how these techniques can be used effectively across a range of real-world problems
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To develop students’ proficiency in computing techniques appropriate for the analysis of economic data
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To provide hands-on experience, using real-world data, and apply economic reasoning to policy issues in a critical manner
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To introduce students to empirical academic research and develop their critical appraisal skills
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To help students develop and consolidate skills that are transferable to other modules and to the workplace
Module learning outcomes
At the end of the module, you will:
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Show a good understanding of OLS regression methods on cross-sectional and time series data, how these methods can be used to test economic hypotheses and be able to discuss their limitations
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Show familiarity with advanced econometric methods such as Panel Data methods and Instrumental Variables estimation methods
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Be proficient in Stata (data management, estimating models, testing hypotheses)
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Be able to perform and interpret an econometric analysis using real-world data
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Be able to understand and evaluate applied economic papers which use standard econometric methods
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Have developed experience of working in a team and meeting deadlines
Indicative assessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 10 |
| Essay/coursework | 90 |
Special assessment rules
None
Additional assessment information
Opportunities for formative assessment
-
Written feedback on two assignments (preparation for tutorials)
-
Oral feedback during small-group tutorial sessions
Indicative reassessment
| Task | % of module mark |
|---|---|
| Essay/coursework | 90 |
Module feedback
Feedback on both components of assessment
-
Qualitative individual feedback (marking grid) at the same time as the mark
-
Cohort feedback after the last submission
Indicative reading
Main textbook
J. Wooldridge Introductory Econometrics. A Modern Approach, Cengage
Indicative reading
D. Gujarati, D. Porter Essential of Econometrics