This course aims to provide students with foundational skills in data analysis for applied econometrics, with a focus on the use of R. By the end of the course, students will be able to: build and manage databases from heterogeneous data sources; clean, reorganize, and integrate data for econometric analysis; apply exploratory data analysis techniques; prepare data for the estimation of econometric models.
Teaching Methods
The course is mainly computer-based and will be held in a computer lab.
Assessment Methods
Practical Exam
Texts
Main references: - Lecture slides available on Kiro Additional material. These contents do not perfectly match the course's topics, so remember to take as a “reference” the topic discussed together: https://modern-rstats.eu/ Chapters: 1-6 https://nkaza.github.io/intro2Rbook/index.html Chapters: 1-5 https://cbdm-01.zdv.uni-mainz.de/~stalbrec/RcourseData/htmls/R_Tuto_ggplot_extra.nb.html https://rafalab.dfci.harvard.edu/dsbook/tidyverse.html Chapters 4, 5.1, 8, 9, 10, 18 [Note that this reference uses the new syntax for pipes, so \verb+|>+ instead of \verb+%>%+. They are equivalent.] https://www.econometrics-with-r.org/ Sections: 4.1, 4.2, 6.2, 6.3, 8, 10.3, 10.4 https://www.zeileis.org/teaching/AER/ Section: “Linear Regression”
Contents
The course is structured in two parts. In the first part – Data Manipulation includes: R basics: installation, packages, scalars, vectors, matrices, data frames, basic operations, loops Introduction to the tidyverse: tibbles, pipes, data manipulation functions (mutate, group_by, etc.) Advanced data wrangling: joins (inner, left, right, full), reshaping data (pivot_longer, pivot_wider), applying functions across columns Data visualization using ggplot2: basic plotting, customization, themes Working with spatial data: shapefiles, simple geometry operations Creating publication-quality tables: gt, tbl_summary, tbl_regression The second part – Applied Econometrics – focuses on the use of appropriate econometric techniques to analyze the datasets built in the first part. Students will learn how to implement standard microeconometric methods in R and how to interpret the results in an applied context.
