Teaching
Teaching is organized around applied skills, transparent reasoning, reproducible analysis, and policy communication. Course details remain high-level unless a term, syllabus, or public teaching link has been verified.
Teaching philosophy
Prof. Gulseven’s teaching connects economic theory with applied data, careful interpretation, and policy communication. Students are encouraged to build reproducible workflows, explain assumptions clearly, and translate empirical results into concise research notes, policy briefs, or classroom presentations.
Teaching methods at a glance
R and Python Teaching
Students use R and Python for data cleaning, visualization, regression, simulation, and reproducible reporting. The emphasis is on applied interpretation rather than software mechanics alone.
Active Learning
Classroom activities include discussion-based problem solving, games, simulations, short applied exercises, and guided interpretation of empirical results. The goal is to make abstract economic models more concrete and policy relevant.
Project-Based Learning
Students apply economic concepts through structured projects, trade simulations, empirical exercises, and policy briefs. TINA simulations are used especially in trade courses to help students connect theory, data, and policy decisions.
Courses and teaching areas
Graduate Econometrics
Graduate-level empirical methods, research design, regression modeling, panel data, time-series analysis, reproducibility, and interpretation of econometric evidence.
International Agricultural Trade
International trade, WTO agreements, regional integration, food security, non-tariff measures, gravity-model intuition, TINA simulations, and policy briefs.
Applied Agricultural Econometrics
Applied regression workflows for agricultural, resource, food-security, and policy questions using public or teaching-approved datasets.
Agricultural Finance and Risk
Agricultural finance, insurance, household or producer risk, financial literacy, savings-based finance, sustainable development, and applied quantitative examples.
Data Visualization and Quantitative Methods
Economic communication, reproducible charts, statistics, empirical methods, public data, Python, R, Google Colab, and Quarto.
Student Research and Policy Communication
Student projects, research proposals, policy briefs, presentation design, data documentation, and active learning based on reviewed public materials.
Tools and methods
R Python Google Colab Quarto LaTeX Git/GitHub panel data time series PPML gravity models TINA simulations policy briefs data visualization
Student project examples
- TINA-based FTA or regional-integration simulation exercises for international trade courses.
- Food-security and price-transmission visualizations using approved public data.
- Applied agricultural econometrics projects using regression, panel-data, or time-series methods.
- Policy-brief assignments that summarize empirical evidence for non-specialist readers.
- Python/R notebooks that reproduce charts, tables, and interpretation notes.
Teaching innovation
Teaching innovation emphasizes active learning, transparent computational workflows, simulation-based trade policy exercises, reproducible notebooks, and student research projects. AI-assisted tools may support drafting, coding, and formatting, but students remain responsible for verification, citations, interpretation, and academic integrity.
TINA Simulation
Students use the Trade Intelligence & Negotiation Adviser to move from tariff theory to HS6 trade data, tariff simulation, and negotiation-oriented policy interpretation.
Public-materials policy
- Do not post exams, grades, private student records, or restricted institutional materials.
- Use public or teaching-approved datasets only.
- Label planned materials as
[coming soon]until reviewed. - Keep notebooks, policy briefs, and visualization examples reproducible and text-first where possible.