The U.S. Dollar in Crisis: The Role of Asset-Backed Digital Currencies in Its Transformation
Description
This dataset supports the thesis The U.S. Dollar in Crisis: The Role of Asset-Backed Digital Currencies in Its Transformation by Nicolin Decker. It provides empirical data and econometric models to analyze the feasibility of Asset-Backed Digital Currencies (ABDCs) as a stabilizing alternative to fiat monetary systems. Spanning historical macroeconomic data (1970–2024) and projected ABDC circulation trends (2026–2036), the dataset includes inflation-adjusted monetary indicators, crisis response simulations, and global trade impact assessments. Key analyses incorporate Vector Autoregression (VAR), Monte Carlo simulations, Granger causality tests, and DSGE modeling to evaluate ABDC's effect on inflation control, liquidity stability, and financial resilience. The dataset is structured for full reproducibility, ensuring rigorous validation of ABDC’s role in modernizing global monetary policy.
Files
Steps to reproduce
1. Install Dependencies Ensure Python 3.9+ is installed along with the required libraries: ```pip install pandas numpy statsmodels matplotlib seaborn scikit-learn scipy sympy arch``` 2. Load the Dataset Import the required modules and load the dataset files: ```import pandas as pd macro_df = pd.read_csv("historical_macroeconomic_data.csv") abdc_df = pd.read_csv("abdc_circulation_liquidity.csv")``` 3. Data Preprocessing & Alignment - Check for missing values: ```print(macro_df.isnull().sum(), abdc_df.isnull().sum())``` - Align overlapping years: ```common_years = set(macro_df["Year"]).intersection(abdc_df["Year"]) macro_df = macro_df[macro_df["Year"].isin(common_years)] abdc_df = abdc_df[abdc_df["Year"].isin(common_years)]``` 4. Run Regression Analysis Assess ABDC circulation vs. inflation and GDP: ```import statsmodels.api as sm X = macro_df[["Inflation Rate (%)", "GDP Growth Rate (%)"]] X = sm.add_constant(X) y = abdc_df["Projected ABDC Circulation (Trillions)"] model = sm.OLS(y, X).fit() print(model.summary())``` 5. Monte Carlo Simulations Simulate 10,000 economic conditions: ```import numpy as np simulations = 10000 sim_inflation = np.random.normal(macro_df["Inflation Rate (%)"].mean(), macro_df["Inflation Rate (%)"].std(), simulations) sim_gdp = np.random.normal(macro_df["GDP Growth Rate (%)"].mean(), macro_df["GDP Growth Rate (%)"].std(), simulations) X_sim = sm.add_constant(np.column_stack((sim_inflation, sim_gdp))) y_pred = model.predict(X_sim)``` 6. Granger Causality Test for ABDC vs. USD Trade Dominance ```from statsmodels.tsa.stattools import grangercausalitytests grangercausalitytests(trade_df[["ABDC Trade Share (%)", "USD Trade Share (%)"]], maxlag=3)``` 7. Validate Results & Interpret Findings - Ensure Monte Carlo simulations confirm ABDC resilience. - Check p < 0.05 in Granger causality tests to confirm ABDC’s trade impact. This workflow ensures full reproducibility of the dataset's econometric findings along with the included document: Reproducibility Instructions and Mathematical Validation.