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Who will sign a double tax treaty next? A prediction based on economic determinants and machine learning algorithms
16 references·0 citing·54 second hop
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References

  • 2014Big Data: New Tricks for Econometrics1,608 cited
  • 2011The Gravity Model1,057 cited
  • 2019Machine learning in energy economics and finance: A review463 cited
  • 2020Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach259 cited
  • 2000Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications257 cited
  • 2004The Effects of Bilateral Tax Treaties on U.S. FDI Activity237 cited
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  • 2021Machine Learning in Economics and Finance176 cited
  • 2010Networks as Channels of Policy Diffusion: Explaining Worldwide Changes in Capital Taxation, 1998-2006167 cited
  • 2004People's Republic of China's Round-Tripping FDI: Scale, Causes and Implications140 cited
  • 2003Asymmetric FDI and tax-treaty bargaining: theory and evidence137 cited
  • 2018Tax Fraud Detection for Under-Reporting Declarations Using an Unsupervised Machine Learning Approach85 cited
  • 2018Targeting with machine learning: An application to a tax rebate program in Italy84 cited
  • 2018When Do Developing Countries Negotiate Away Their Corporate Tax Base?71 cited
  • 2021How effective is carbon pricing?—A machine learning approach to policy evaluation

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64 cited
  • 2020Tax Default Prediction Using Feature Transformation-Based Machine Learning62 cited
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