Data & Code

Data, code, and reproducible research methods for applied economics, trade, food security, finance, sustainability, and teaching resources.
Author

Osman Gulseven

Keywords

data and code, Osman Gulseven, PPML, gravity model, wavelet coherence, R, Python, Quarto, Google Colab, data visualization

This section documents data and code plans for reproducible research and teaching. Public resources will be added only after data sources, licensing, confidentiality, and usage rights are verified.

Method library

Trade and policy models

PPML gravity estimation, GPML, structural gravity examples, CEPA and FTA simulation notes, non-tariff-measure templates, and TINA teaching exercises.

Food and price analytics

Wavelet coherence, price-transmission analysis, time-series visualization, commodity-market indicators, and Oman food-security teaching examples.

Consumer and resource economics

Hedonic demand modeling, willingness-to-pay analysis, non-market valuation, adoption models, survey-based examples, and sustainability indicators.

Finance and risk

Portfolio analysis, quantile regression, volatility analysis, time-series decomposition, and financial-market examples for research and teaching.

Planned code resources

  • R templates for econometric modeling and visualization.
  • Python notebooks for applied data visualization and teaching.
  • Google Colab notebooks for student exercises.
  • TINA simulation note templates for trade-policy scenarios.
  • Quarto templates for reproducible research reports.
  • LaTeX and Beamer examples for academic writing and presentations.

No private datasets, confidential student records, restricted institutional files, copyrighted PDFs, or generated binary artifacts should be committed to this repository.