Econometrics

Content

Objective

The aim of the course is to equip MA students in economics with the fundamental tools of econometric analysis and to develop their ability to apply and interpret these tools with real economic data in R. In this context, the following topics will be covered and the following skills will be developed:
• Organizing, cleaning, transforming, and visualizing data with the R software using modern graphical techniques.
• Learning the logic of reproducible analysis early on through R Markdown/Quarto.
• Constructing and interpreting simple and multiple linear regression models in line with research questions.
• Critically interpreting statistical inference tools such as standard errors, confidence intervals, t-statistics, and p-values.
• Validating inference through resampling-based alternative approaches via bootstrap and permutation tests.
• Analyzing group differences and conditional relationships using dummy variables and interaction terms.
• Introduction to advanced topics in regression analysis: model selection, functional form, variable scaling, and structural change tests.
In this course, students are not expected to memorize formulas; rather, they are expected to select data
appropriate to the research question, analyze it in the R environment, critically interpret the results, carry out an individual analysis process, and report this process in a reproducible manner. Students may use AI tools in coding, debugging, and interpretation, but they are obliged to explain and defend every analysis step they use.

Course Content

The course begins with setting up a reproducible data analysis process in the R/RStudio and Quarto/R
Markdown environment. In the second week, data flow in R (input-output) is covered. It then covers data
management, descriptive analysis, and visualization with ggplot2. Simple and multiple linear regression,
statistical inference, and bootstrap methods are then taught. At the end of the semester, dummy variables,
interaction terms, and advanced topics in regression analysis — adjusted R2 and model selection, functional
form (level–log comparison, RESET test), variable scaling, beta coefficients, and structural change (Chow test)
— are covered.

Course Learning Outcomes

Upon successful completion of this course, the student will be able to:
1. Set up a reproducible data analysis process in the R/RStudio and R Markdown/Quarto environment; carry out data loading, cleaning, transformation, and reporting steps in an integrated manner.
2. Organize a real economic dataset, explore it with descriptive statistics and ggplot2 graphics, and interpret the basic patterns in the data contextually.
3. Construct simple and multiple linear regression models, interpret coefficients, explain the role of control variables, and critically evaluate model outputs.
4. Perform statistical inference and apply hypothesis tests; validate inference results by applying bootstrap and permutation tests in the R environment.
5. Analyze group differences and conditional relationships within a regression framework using dummy variables and interaction terms; support the results with graphs and interpret them.
6. Explain advanced topics in regression analysis (adjusted R2 and model selection, functional form and level–log comparison, variable scaling, beta coefficients, and structural change/Chow test) and apply these tools in the R environment.
7. Define analysis responsibilities in the contribution statement in their individual project, carry out report and poster production independently, and explain their own analysis process in the oral defense.

Teaching and Learning Methods

• Computer-assisted applied instruction: live data analysis, data cleaning, visualization, and reporting with R/RStudio.
• Conceptual discussion and intuitive learning: classroom dialogue centered on research questions, method selection, and interpretation rather than formula memorization.
• Case analysis on real-world datasets: Eurostat, OECD, Penn World Table, World Bank, Gapminder, World Values Survey, etc.
• Short, application-oriented learning activities with instant feedback; students may use AI during the analysis process, but must be able to explain and defend every step used.
• In-class and individual feedback: critical evaluation of students’ code, tables, figures, and interpretations by the instructor and classmates; each student defends their own analysis process.

References

1. Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (2023). R for Data Science (2nd ed.). Open-access foundational resource for data management, cleaning, transformation, and visualization with R.
https://r4ds.hadley.nz/ (English) and https://tr.r4ds.hadley.nz/ (Turkish)
2. Wooldridge, J. M. (2020). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. For the datasets and R code used in the book, see
https://justinmshea.github.io/wooldridge/
3. Some datasets and R packages you may find useful:
• Eurostat: European Union statistics:
https://ec.europa.eu/eurostat/web/main/data/database, R packages: eurostat, restatapi.
• OECD Data Explorer: Economic and social indicators:
https://www.oecd.org/en/data.html, R packages: readoecd, OECD.
• Penn World Table (PWT):
https://www.rug.nl/ggdc/productivity/pwt/, R package: pwt10.
• World Bank Open Data: Development indicators:
https://data.worldbank.org/, R package: WDI.
• World Values Survey:
https://www.worldvaluessurvey.org/
• European Social Survey:
https://www.europeansocialsurvey.org/, R package: essurvey
• For other data easily downloadable and explorable via R packages, see
https://github.com/rOpenGov

Weekly Contents

Assessment System

ECTS