Course Contents

The aim of this course is to enable students to understand advanced econometric methods and apply appropriate econometric models to economic data using R/RStudio. The course covers model specification, heteroskedasticity, time-series regressions, serial correlation, stationarity and persistence, dynamic models, endogeneity, instrumental variables, two-stage least squares, and limited dependent variable models. Students are expected to estimate econometric models, test model assumptions, apply appropriate corrective methods, and interpret empirical findings from an economic perspective.

There is no formal prerequisite. However, students are expected to have basic knowledge of statistics, regression analysis, and econometrics and to be familiar with multiple linear regression, hypothesis testing, and basic R/RStudio applications.

Heiss, F. (2020). Using R for Introductory Econometrics (2nd ed.). Independently Published.

Students are expected to comply with the principles of academic integrity in all examinations, coursework, and other academic activities. Information, data, tables, figures, code, and ideas obtained from external sources must be properly acknowledged. Plagiarism, manipulation of data or results, completing work on behalf of another student, and unauthorized collaboration are considered violations of academic integrity. Artificial intelligence tools may be used, when permitted by the course instructor, to clarify econometric concepts, assist with R coding, or explore alternative analytical methods. Students are responsible for verifying the accuracy of AI-generated code, econometric results, and interpretations. AI tools may not replace students' own econometric analysis, model selection, or economic interpretation.