Targeting policy-compliers with machine learning: an application to a tax rebate programme in Italy
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Cited by:
- Andreas Joseph, 2019.
"Parametric inference with universal function approximators,"
Papers
1903.04209, arXiv.org, revised Oct 2020.
- Joseph, Andreas, 2019. "Parametric inference with universal function approximators," Bank of England working papers 784, Bank of England, revised 22 Jul 2020.
- Concetta Rondinelli & Roberta Zizza, 2020. "Spend today or spend tomorrow? The role of inflation expectations in consumer behaviour," Temi di discussione (Economic working papers) 1276, Bank of Italy, Economic Research and International Relations Area.
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- Michele Rabasco & Pietro Battiston, 2023. "Predicting the deterrence effect of tax audits. A machine learning approach," Metroeconomica, Wiley Blackwell, vol. 74(3), pages 531-556, July.
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More about this item
Keywords
machine learning; prediction; programme evaluation; fiscal stimulus;All these keywords.
JEL classification:
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- H3 - Public Economics - - Fiscal Policies and Behavior of Economic Agents
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2018-01-08 (Big Data)
- NEP-CMP-2018-01-08 (Computational Economics)
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