Quantitative Economics and Data Science Project Collection: Empirical Research from ECB Monetary Policy to Structural Modeling

This project collection covers financial econometrics, structural modeling, and machine learning applications, including three core projects: VECM analysis of the European Central Bank (ECB)'s unconventional monetary policy, Bresnahan-Reiss market entry model estimation, and empirical research on the impact of Brexit on firm heterogeneity. Using rigorous academic methods and tools like R, Stata, and MATLAB, the projects demonstrate the complete research process from data cleaning to model construction and result interpretation, combining theoretical depth with practical value.

• Original Author/Maintainer: hiddedeklerk-cpu
• Source Platform: GitHub
• Original Title: portfolio
• Original Link: https://github.com/hiddedeklerk-cpu/portfolio
• Release Date: May 28, 2026

This GitHub repository showcases a series of high-quality quantitative economics and data science projects, reflecting a solid foundation in econometric theory and covering macroeconomic policy, market entry behavior, and analysis of the impact of major political events on financial markets.

Research Background of ECB's Unconventional Monetary Policy

From 2015 to 2018, the ECB implemented unconventional monetary policies such as negative interest rates and quantitative easing (QE). The transmission mechanism and effects of these policies on the Eurozone stock market and exchange rates are the focus of academic and policy attention. It is necessary to verify the applicability of traditional theories under the zero lower bound and the impact of QE through the asset price channel.

Research Background of the Bresnahan-Reiss Model

The Bresnahan-Reiss (1991) binary entry model is an important tool for industrial organization analysis of market structure. It is necessary to estimate key parameters such as fixed costs and marginal costs to understand firm entry decisions.

Research Background of Brexit Impact

After the 2016 UK Brexit referendum, firms showed significant heterogeneity in their responses to the shock. It is necessary to explore the impact of firm characteristics such as internationalization level and industry attributes on shock sensitivity.

ECB Project Methods

A three-variable Vector Error Correction Model (VECM) is used, with steps including: unit root test to determine the order of integration, Johansen cointegration test to identify long-term equilibrium relationships, estimation of VECM parameters and calculation of impulse response functions, and Robinson (1995) long-memory test to detect long-term dependence. Tools include R (urca, vars packages) and Stata for cross-validation.

Bresnahan-Reiss Project Methods

Implement Maximum Likelihood Estimation (MLE) from scratch, write code for gradient and Hessian matrix calculations, and extend to the Berry (1992) simulation framework to introduce firm heterogeneity. Cross-validation of results via MATLAB and Stata.

Brexit Project Methods

Use event study methodology to calculate abnormal returns of firms, combined with cross-sectional regression to analyze the impact of firm characteristics; tool used is Stata.

Technology Stack

Python (data cleaning, ML), R (time series, visualization), MATLAB (numerical computation), Stata (econometric analysis), SQL (database), Excel/Power BI (visualization).

• ECB Project: VECM captures the long-term equilibrium and short-term dynamic adjustment between monetary policy, stock market, and exchange rate;
• Bresnahan-Reiss Project: Fully implemented core algorithms, multi-software cross-validation ensures reliable results, and received 100% academic evaluation;
• Brexit Project: Firm characteristics such as internationalization level, industry attributes, financial leverage, and size significantly affect their sensitivity to the Brexit shock;
• All projects enhance result reliability through multi-tool cross-validation.

This project collection has the following values:

1. Solid Theory: Each project is supported by clear economic theories, and the models comply with academic norms;
2. Methodological Innovation: Extends standard methods (e.g., introducing firm heterogeneity, long-memory test);
3. Complete Implementation: Implements core algorithms from scratch and verifies with multiple software;
4. In-depth Interpretation: Not only reports statistical results but also interprets economic implications.

It is an excellent reference example for students and provides methodological and framework references for financial analysts and economic research professionals.