Data and the verified directional signature | Test 1 (SOCH-A) and Test 3 (SOCH-C) on the eight-market sample | Test 4: endogenous classification | Test 2 (SOCH-B): the sharp shape-symmetry test

The mechanism | From spectrum to wavelet scales | Three falsifiable predictions | Identification

Overview | 1. The detection-attribution distinction | 2. Stage 1: WQTE math intuition | Transfer entropy | Wavelet decomposition | Quantile conditioning | Bias correction | 3. Stage 2: why multi-method identification? | 4. IV/2SLS with channel-specific instruments | 5. LASSO instrument selection | 6. Local projections | 7. Rigobon heteroskedasticity-based identification | 8. Cinelli-Hazlett robustness value | 9. Identification-status classification | Session info

Overview | 1. Setup | 2. Loading the bundled datasets | 3. Building the v2 channel composites | 4. Stage 1: WQTE detection | Absolute thresholding | 5. Stage 1 results: density and centrality (Table 1) | 6. Stage 2: IV/2SLS attribution (Table 2) | 7. Cross-method comparison: LP and Rigobon (Table 6) | Sargan over-identification | 8. Bootstrap confidence intervals | 9. Cinelli-Hazlett robustness values | 10. Visualisation walkthrough (Figures 1-7) | 11. Walktrap communities | 12. End-to-end pipeline | Session info

Overview | 1. Required data structure | 2. Custom market list and crisis periods | 3. Custom channel composites | 4. Calling the pipeline | 5. Adapting visualisations for custom data | 6. Worked example: synthetic five-market panel | Common pitfalls | Session info

Overview | 1. The detection-attribution distinction | 2. Stage 1: WQTE math intuition | Transfer entropy | Wavelet decomposition | Quantile conditioning | Bias correction | 3. Stage 2: why multi-method identification? | 4. IV/2SLS with channel-specific instruments | 5. LASSO instrument selection | 6. Local projections | 7. Rigobon heteroskedasticity-based identification | 8. Cinelli-Hazlett robustness value | 9. Identification-status classification | Session info

Overview | 1. Setup | 2. Loading the bundled datasets | 3. Building the v2 channel composites | 4. Stage 1: WQTE detection | Absolute thresholding | 5. Stage 1 results: density and centrality (Table 1) | 6. Stage 2: IV/2SLS attribution (Table 2) | 7. Cross-method comparison: LP and Rigobon (Table 6) | Sargan over-identification | 8. Bootstrap confidence intervals | 9. Cinelli-Hazlett robustness values | 10. Visualisation walkthrough (Figures 1-7) | 11. Walktrap communities | 12. End-to-end pipeline | Session info

Overview | 1. Required data structure | 2. Custom market list and crisis periods | 3. Custom channel composites | 4. Calling the pipeline | 5. Adapting visualisations for custom data | 6. Worked example: synthetic five-market panel | Common pitfalls | Session info

Philosophy: From Data to Instruments | Six Dimensions of Real Instruments | Dimension 1: Geographic Instruments | Dimension 2: Technology Instruments | Dimension 3: Migration Instruments | Dimension 4: Geopolitical Instruments | Dimension 5: Financial Instruments | Dimension 6: Natural Risk Instruments | Composite Instrument Creation | Alternative State-of-the-Art Instruments | Spatial and Network Instruments | Bartik and Shift-Share Instruments | Judge Historical Instruments (Best Performing!) | Instrument Validation Framework | 1. Strength Testing (F-statistics) | 2. Relevance Testing | 3. Exogeneity Testing | Country-Specific Examples | High-Income Countries | Transition Economies | Emerging Markets | Advanced Techniques | 1. Time-Varying Instruments | 2. Income-Specific Instruments | 3. Regional Interactions | Best Practices for Instrument Creation | 1. Multiple Instrument Approaches | 2. Historical vs. Contemporary | 3. Geographic vs. Institutional | Common Issues and Solutions | Issue 1: Weak Instruments (F < 10) | Issue 2: Overidentification Rejection | Issue 3: Limited Cross-Country Variation | Empirical Validation Results | Top 10 Strongest Instruments | Diagnostic Summary | Conclusion

What is the Environmental Phillips Curve? | Package Overview | 🔧 24 Instrumental Variable Approaches | 🌐 Transfer Entropy Causal Discovery | 📊 7 Publication-Quality Visualizations | 📈 Comprehensive Econometric Framework | Key Features | ✅ Methodological Innovation | ✅ Empirical Validation | Quick Start | Usage | Basic Usage | Create Instruments from Your Data | Transfer Entropy Analysis | Econometric Analysis | Visualization | Methodological Advantages | 1. Multiple Identification Strategies | 2. Theory-Driven Instrument Creation | 3. Network-Based Innovation | Empirical Performance | Instrument Strength Results | Strength Classification | Transfer Entropy Results | Real-World Applications | 1. Policy Analysis | 2. Academic Research | 3. International Organizations | Package Structure | Core Functions | Data | Getting Started | 1. Explore the Vignettes | 2. Run the Examples | 3. Use Your Own Data | Citation | BibTeX Entry | Support and Contribution | Getting Help | Contributing | Conclusion

Overview of Network Analysis in ManyIVsNets | Network Types and Applications | 1. Transfer Entropy Networks (Variable-Level) | 2. Country Income Classification Networks | 3. Cross-Income CO2 Growth Nexus | 4. Migration Impact Networks | 5. Instrument Causal Pathways | 6. Regional Networks | 7. Instrument Strength Comparison | Comprehensive Network Analysis Results | Key Findings from Network Analysis | Network Visualization Best Practices | 1. Layout Algorithms | 2. Color Schemes | 3. Node and Edge Sizing | Advanced Network Analysis | 1. Network Metrics | 2. Community Detection | 3. Network Evolution Analysis | Complete Network Analysis Workflow | Step 1: Data Preparation | Step 2: Transfer Entropy Analysis | Step 3: Create All Network Visualizations | Step 4: Instrument Strength Analysis | Empirical Results Summary | Network Analysis Performance | Top Performing Instruments | Conclusion | Future Extensions

What is Transfer Entropy? | Why Transfer Entropy for Environmental Economics? | Variables in Our Transfer Entropy Analysis | Transfer Entropy Implementation | Data Preparation | Enhanced Transfer Entropy Calculation | Transfer Entropy Matrix Construction | Our Transfer Entropy Results | Transfer Entropy Matrix | Key Causal Relationships Identified | Network Properties | Transfer Entropy Network Construction | Network Creation Process | Network Visualization | Country Network Construction from Transfer Entropy | Country Similarity Matrix | Network-Based Instruments Creation | Transfer Entropy Instrument Performance | TE-Based Instrument Strength | Country Network Properties | Methodological Advantages | 1. Non-Parametric Causal Discovery | 2. Directional Causality | 3. Network-Based Instruments | Robustness Checks | 1. Alternative Entropy Measures | 2. Lag Length Sensitivity | 3. Sample Period Robustness | Policy Implications | 1. Economic Growth-Environment Nexus | 2. Labor Market Dynamics | 3. Energy Transition Effects | Comparison with Traditional Methods | Transfer Entropy vs. Granger Causality | Empirical Comparison | Advanced Applications | 1. Dynamic Network Analysis | 2. Conditional Transfer Entropy | 3. Multivariate Transfer Entropy | Conclusion