INTERNATIONAL ECONOMICS WITH R

Course Information
TitleΔΙΕΘΝΗΣ ΟΙΚΟΝΟΜΙΑ ΜΕ ΤΗΝ R / INTERNATIONAL ECONOMICS WITH R
CodeΕΔΕ09
FacultySocial and Economic Sciences
SchoolEconomics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter/Spring
CoordinatorDimitrios Ntantakas
CommonNo
StatusActive
Course ID600026523

Programme of Study: Data Science in Economics and Finance

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
CoreElective Courses116
Class Information
Academic Year2025 – 2026
Class PeriodWinter
Faculty Instructors
Class ID
600273578
Course Type 2021
Specialization / Direction
Mode of Delivery
  • Face to face
  • Distance learning
Digital Course Content
Language of Instruction
  • Greek (Instruction, Examination)
Learning Outcomes
Upon successful completion of the course, students will be able to: Understand and explain fundamental theories of international economics, such as trade theories, exchange rate policies, and financial integration, linking them to real-world economic phenomena. Apply econometric techniques to analyze international economic data, assessing relationships between variables and estimating models of trade and investment flows. Use data analysis tools and methods taught in the graduate program, such as R and machine learning techniques, to extract insights from large international economic datasets. Integrate theoretical knowledge, econometric analysis, and modern data analytics to understand and solve complex international economic problems. Analyze policies and economic issues at the international level, evaluating their impacts on the economy and financial markets based on evidence and data. Present and communicate data analysis results effectively, using appropriate charts, tables, and reports for economic topics.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Work in an international context
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
The course examines the fundamental theories and policies of International Economics, as well as modern methods for analyzing international economic data using the R programming language. It is based on a comprehensive approach that combines theory, econometrics, and data analysis, enabling students to analyze international economic phenomena and evaluate policies and strategies using real-world data. The course includes lectures, workshops, and a seminar project with practical applications on datasets from international markets and economic indicators. International Trade and Trade Policies Theory: Classical and modern trade theories, tariffs, quotas, trade agreements Econometrics: Gravity models, panel data, analysis of trade policy impacts Data Analysis: Trade data visualization, data cleaning, application of predictive models International Capital Flows and Financial Markets Theory: Foreign Direct Investment (FDI), financial integration, crisis impacts Econometrics: Panel regressions, market correlation analysis, stress testing Data Analysis: Portfolio simulations, machine learning prediction models, capital flow visualization European Union and Economic Integration Theory: EU institutional framework, single market, monetary union, cohesion policies Econometrics: Estimating the effects of integration on economies using panel and time series data Data Analysis: Analysis of EU country economic indicators, visualizations, scenario analysis Foreign Exchange Markets and Monetary Policy Theory: Exchange rate regimes, interest rate policies, currency interventions Econometrics: Time series, ARIMA/GARCH models, exchange rate equilibrium models Data Analysis: Historical data analysis, forecasting, hedging simulations
Educational Material Types
  • Notes
  • Slide presentations
  • Interactive excersises
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
  • Use of ICT in Student Assessment
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39✓
Reading Assigment60✓
Written assigments81✓✓
Total180
Student Assessment
Description
Exercises, class presentations and Class Assignments Exercises: During laboratory sessions and seminars, students will complete hands-on exercises in R to analyze international economic data, create visualizations, implement econometric models, and apply forecasting techniques. These exercises aim to reinforce theoretical knowledge through practical experience. Class Presentations: Students will prepare short presentations (5–10 minutes) on selected topics from the four course modules, analyzing both theoretical concepts and practical applications, and fostering discussion and critical thinking. Assignments: Students are expected to complete a seminar project or written assignment that applies course concepts to real-world datasets. The project may include data analysis, econometric modeling, visualization, and policy evaluation, culminating in a written report and/or presentation.
Student Assessment methods
  • Written Assignment (Formative)
  • Oral Exams (Formative)
  • Labortatory Assignment (Formative)
Bibliography
Course Bibliography (Eudoxus)
Data Science for Economics and Finance: Methodologies and Applications - DOI 10.1007/978-3-030-66891-4 Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking DOI 10.5555/9781449361327 Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python DOI 10.5555/9781492072942 Machine Learning for Finance: Principles and Practice for Financial Analysts, Quantitative Analysts, and Financial Engineers DOI 10.5555/9781789136369 International Economics: Theory and Policy (10th Edition) – Paul Krugman, Maurice Obstfeld, Marc Melitz DOI: 10.5555/9780133423646 Introduction to Econometrics with R – Martin Christopher Arnold & Christoph Hanck DOI: 10.1007/978-3-030-66891-4 R for Economic Research – R. L. Ripio R-ithmetic for Economists: A Data-Driven Approach Introduction to Mathematics for Economics with R – Springer DOI: 10.1007/978-3-031-05202-6
Additional bibliography for study
Data Science for Economics and Finance: Methodologies and Applications - DOI 10.1007/978-3-030-66891-4 Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking DOI 10.5555/9781449361327 Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python DOI 10.5555/9781492072942 Machine Learning for Finance: Principles and Practice for Financial Analysts, Quantitative Analysts, and Financial Engineers DOI 10.5555/9781789136369 International Economics: Theory and Policy (10th Edition) – Paul Krugman, Maurice Obstfeld, Marc Melitz DOI: 10.5555/9780133423646 Introduction to Econometrics with R – Martin Christopher Arnold & Christoph Hanck DOI: 10.1007/978-3-030-66891-4 R for Economic Research – R. L. Ripio R-ithmetic for Economists: A Data-Driven Approach Introduction to Mathematics for Economics with R – Springer DOI: 10.1007/978-3-031-05202-6
Last Update
03-09-2025