Specialising in macroeconomics, econometrics, and data science for economic analysis.

• Location: I am based in London, UK.
• Language Skills: Fluent in both English and Spanish.
• Academic Background: I am strongly grounded in financial mathematics and economics, with particular interests in macro-finance, international trade, and advanced econometric methods.
• Current Focus:
  • Applying machine learning approaches (e.g., random forests, gradient boosting, and deep learning) to improve macroeconomic forecasting.
  • Investigating global financial markets through dynamic time-series modelling (e.g., VAR, SVAR, FAVAR).
  • Delving into international trade determinants using a blend of classical econometric and data-driven frameworks.
• Skills in Development:
  • Advanced Python workflows for large-scale data analysis (including distributed computing).
  • Bayesian estimation, GMM, and other cutting-edge econometric methods.

Here are some examples—actual or forthcoming—of the work I pursue:

1. Macro-Finance Analysis

   • Goal: Examine how shifts in monetary policy influence asset prices, using both structural and reduced-form VAR techniques.
   • Tools: Python (statsmodels, linearmodels), with potential machine learning enhancements.

2. International Trade & Gravity Models

   • Goal: Identify key drivers of bilateral trade flows, employing the classical gravity framework and modern ML algorithms.
   • Tools: Python or R, focusing on panel data and advanced regression methods.

3. Asset Pricing & GMM

   • Goal: Investigate standard factor models (CAPM, Fama-French) in a GMM framework, potentially augmented by ML-based factor selection.
   • Tools: Python (pandas, numpy, arch or linearmodels), or R for empirical finance.

4. Causal Inference with Machine Learning

   • Goal: Implement methods such as double machine learning or causal forests to evaluate policy interventions (e.g., minimum wage changes).
   • Comparisons: Benchmark against classical approaches (e.g., Diff-in-Diff, Synthetic Control) to highlight ML advantages.

• Econometrics: statsmodels, linearmodels, arch, plm (R), lfe (R)
• Machine Learning: scikit-learn, xgboost, pytorch, tensorflow
• Data Management: pandas, dplyr
• Visualisation: matplotlib, seaborn, ggplot2, plotly

• GitHub: Feel free to open an Issue on any of my repositories for feedback or enquiries.
• Email: [email protected]
• X (formerly Twitter): @AscendedYield
• Substack: Lambeth Economic Review

Thank you for visiting my profile. Please do explore my repositories or reach out if you would like to collaborate on data-intensive or econometrically rigorous projects.