ManyIVsNets
Environmental Phillips Curve Analysis with Multiple Instrumental Variables and Networks
Comprehensive toolkit for Environmental Phillips Curve analysis
featuring multidimensional instrumental variable creation, transfer entropy
causal discovery, network analysis, and state-of-the-art econometric methods.
Implements geographic, technological, migration, geopolitical, financial,
and natural risk instruments with robust diagnostics and visualization.
Provides 24 different instrumental variable approaches with empirical validation.
Methods based on Phillips (1958)
ManyIVsNets
Overview
ManyIVsNets is a comprehensive R package for Environmental Phillips Curve (EPC) analysis featuring state-of-the-art econometric methods and network analysis.
Key Results based on example data
- 21 out of 24 instrument approaches show strong performance (F > 10)
- Main finding: 1% ↑ unemployment → 0.071% ↓ CO2 emissions
- Network density: Transfer entropy (0.095), Country network (0.25)
Installation
#Install from GitHub
devtools::install_github("avishekb9/ManyIVsNets")
Quick Start
library(ManyIVsNets)
#Run complete analysis pipeline
results <- run_complete_epc_analysis(
data_file = "epc_data_new_ar5_indicators.csv", # Your data file
output_dir = "epc_analysis_results"
)
#View instrument strength results
print(results$strength_results)
Features
- Real multidimensional instruments from economic/geographic data
- Transfer entropy causal discovery using RTransferEntropy
- 24 different instrument approaches tested
- Comprehensive network analysis with country codes
Citation
If you use this package in your research, please cite:
APA Style: Bhandari, A. (2025). ManyIVsNets: Environmental Phillips Curve Analysis with Multiple Instrumental Variables and Networks [Computer software]. GitHub. https://github.com/avishekb9/ManyIVsNets
Chicago Style: Bhandari, Avishek. "ManyIVsNets: Environmental Phillips Curve Analysis with Multiple Instrumental Variables and Networks." GitHub, 2025. https://github.com/avishekb9/ManyIVsNets.