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didTEnets

Transfer Entropy-Informed Difference-in-Differences for Network Analysis

The didTEnets package implements the Transfer Entropy-informed Difference-in-Differences (TE-DiD) framework for policy evaluation in interconnected systems. This methodology combines network analysis with causal inference to measure policy effects on financial contagion and systemic risk transmission.

Overview

Traditional difference-in-differences approaches often fail to capture the complex dynamics of systemic risk transmission in interconnected financial markets. The TE-DiD framework addresses this by:

  • 🌐 Network-Informed Outcomes: Using directed information flows instead of simple returns
  • 📊 Crisis-Focused Analysis: Quantile conditioning to focus on extreme events
  • 🔄 Policy Spillovers: Accounting for spillover effects through network weights
  • 📈 Comprehensive Visualization: Network evolution and policy effect plots

Key Features

  • Transfer Entropy Calculation: Enhanced TE with quantile conditioning and crisis analysis
  • Network Weight Construction: Multiple weight types (correlation, geographic, economic, composite)
  • NICI Construction: Network-Informed Contagion Index as sophisticated outcome variable
  • NIPI Construction: Network-Informed Policy Intensity with spillover effects
  • TE-DiD Estimation: Robust econometric models with two-way fixed effects
  • Network Visualization: Comprehensive plots for network evolution and policy effects

Installation

You can install the development version of didTEnets from GitHub:

# Install devtools if you haven't already
install.packages("devtools")

# Install didTEnets
devtools::install_github("avishekb9/didTEnets")

Quick Start 

library(didTEnets)

# Define financial markets for analysis
tickers <- c("^GSPC" = "USA", "^FTSE" = "GBR", "^GDAXI" = "DEU", 
             "^FCHI" = "FRA", "^N225" = "JPN", "^AXJO" = "AUS")

# Run complete TE-DiD analysis
results <- run_complete_tedid_analysis(
  tickers = tickers,
  start_date = "2019-01-01",
  end_date = "2021-12-31",
  create_plots = TRUE
)

# View main results
summary(results$tedid$main_model)
print(results$summary)

# Access visualizations
if (!is.null(results$plots)) {
  print(results$plots$tedid_plots$timeseries)
  print(results$plots$network_comparison$Crisis)
}

Methodology

The TE-DiD framework implements the following equation:

IncomingFlow_{it} = β₀ + β₁ · EarlyAdopter_i × CrisisPeriod_t + α_i + γ_t + ε_{it}

Where: - IncomingFlow_{it}: Network-Informed Contagion Index (NICI) - the outcome variable - EarlyAdopter_i: Treatment group indicator (early vs. late policy adopters)
- CrisisPeriod_t: Crisis period indicator - β₁: Key coefficient measuring policy effect on financial contagion - α_i: Country fixed effects - γ_t: Time fixed effects

Example Output 

# Sample model results
#                            Estimate Std. Error t-value Pr(>|t|)  
# EarlyAdopter:CrisisPeriod -0.0052    0.0021    -2.476   0.014 *

# Interpretation: Early adoption of stringent policies REDUCED 
# incoming financial contagion by 0.52% during the crisis period

Core Functions

Function Purpose
run_complete_tedid_analysis() Complete analysis pipeline
download_financial_data() Financial data acquisition
calculate_enhanced_transfer_entropy() Transfer entropy calculation
calculate_network_weights() Network weight construction
calculate_nici() Network-Informed Contagion Index
calculate_nipi() Network-Informed Policy Intensity
estimate_tedid_model() TE-DiD model estimation
create_network_comparison() Network visualization

Workflow 

# 1. Data Acquisition
financial_data <- download_financial_data(tickers, "2019-01-01", "2021-12-31")
policy_data <- create_policy_data(financial_data$countries, "2019-01-01", "2021-12-31")

# 2. Network Analysis  
network_weights <- calculate_network_weights(financial_data$returns)
te_matrices <- calculate_te_matrices_by_period(financial_data$returns, period_breaks)

# 3. Variable Construction
nici_data <- calculate_nici(te_matrices, network_weights)
nipi_data <- calculate_nipi(monthly_policy, network_weights)

# 4. TE-DiD Estimation
tedid_dataset <- prepare_tedid_dataset(nici_data, nipi_data, treatment_groups)
tedid_model <- estimate_tedid_model(tedid_dataset)

# 5. Visualization
network_plots <- create_network_comparison(te_matrices)
tedid_plots <- visualize_tedid_results(tedid_dataset)

Applications

The TE-DiD framework is particularly useful for:

  • Crisis Policy Evaluation: Measuring policy effectiveness during financial crises
  • Contagion Analysis: Understanding systemic risk transmission channels
  • Network Evolution: Tracking changes in financial network structure
  • Spillover Assessment: Quantifying cross-border policy effects
  • Systemic Risk Monitoring: Real-time financial stability analysis

Documentation

Sample Data

The package includes sample datasets for testing and demonstration:

  • sample_financial_returns: Daily returns for 6 major stock indices (2019-2021)
  • sample_policy_data: COVID-19 policy stringency data
  • sample_tedid_results: Pre-computed analysis results
# Load and explore sample data
data(sample_financial_returns)
data(sample_policy_data)
head(sample_financial_returns)

Performance Considerations

For large networks (>20 countries) or long time periods:

  • Transfer entropy calculation can be computationally intensive
  • Consider parallel processing for multiple periods/quantiles
  • Use appropriate thresholds for network visualization
  • Cache intermediate results for repeated analysis

Citation

If you use didTEnets in your research, please cite:

@Manual{didTEnets,
  title = {didTEnets: Transfer Entropy-Informed Difference-in-Differences for Network Analysis},
  author = {Avishek Bhandari},
  year = {2024},
  note = {R package version 0.1.0},
  url = {https://github.com/avishekb9/didTEnets}
}

Contributing

Contributions are welcome! Please feel free to submit issues and pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

References

  1. Schreiber, T. (2000). Measuring information transfer. Physical Review Letters, 85(2), 461.
  2. Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press.
  3. Hale, T., Angrist, N., Goldszmidt, R., et al. (2021). A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nature Human Behaviour, 5, 529-538.

The TE-DiD framework builds on several methodological advances:

  • Network Econometrics: Spatial models and spillover analysis
  • Information Theory: Transfer entropy and directed information measures
  • Causal Inference: Difference-in-differences and policy evaluation
  • Financial Networks: Systemic risk and contagion analysis

Support

If you encounter any issues or have questions: