The impact of liquidity conditions on the time-varying link between U.S. municipal green bonds and major risky markets during the COVID-19 crisis: A machine learning approach
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DOI: 10.1016/j.enpol.2023.113911
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More about this item
Keywords
Risky markets; Liquidity conditions; Municipal green bonds; Machine learning models; COVID-19; Shapley additive explanations (SHAP);All these keywords.
JEL classification:
- C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
- Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
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