Economics

The aim of this course is to enable students to understand the fundamental concepts and methods of econometrics and to analyze economic relationships empirically. The course covers simple and multiple linear regression models, the Ordinary Least Squares method, interpretation of regression coefficients, hypothesis testing, confidence intervals, and evaluation of model fit. Students are expected to construct econometric models using economic data, estimate models in R/RStudio, and interpret the resulting findings from an economic perspective.

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Main Textbook:Heiss, F., Using R for Introductory Econometrics, CreateSpace Independent Publishing Platform.Recommended Resources:Wooldridge, J. M., Introductory Econometrics: A Modern Approach, Cengage Learning.Hanck, C., Arnold, M., Gerber, A. & Schmelzer, M., Introduction to Econometrics with R.Kleiber, C. & Zeileis, A., Applied Econometrics with R, Springer.Gujarati, D. N. & Porter, D. C., Basic Econometrics, McGraw-Hill.R and RStudio software, course notes, economic datasets, and R application files.

Students are expected to comply with the principles of academic integrity in all assignments, projects, examinations, and other academic work. 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 support learning, clarify econometric concepts, assist with R coding, or explore analytical methods. However, students remain responsible for verifying the accuracy of AI-generated code, analyses, and interpretations. AI tools may not replace students' own econometric analysis, interpretation, or academic responsibility. Any use of artificial intelligence must comply with the instructor's guidelines and should be disclosed when required.