An efficient Bayes classifier for word classification: an application on the EU Recovery and Resilience Plans
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- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Jeffrey M Wooldridge, 2010.
"Econometric Analysis of Cross Section and Panel Data,"
MIT Press Books,
The MIT Press,
edition 2, volume 1, number 0262232588, April.
- Jeffrey M. Wooldridge, 2001. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262232197, April.
- repec:cii:cepiei:2013-q2-134-3 is not listed on IDEAS
- Phoebe Koundouri & Stathis Devves & Angelos Plataniotis, 2021. "Alignment of the European Green Deal, the Sustainable Development Goals and the European Semester Process: Method and Application," DEOS Working Papers 2113, Athens University of Economics and Business.
- Franca Debole & Fabrizio Sebastiani, 2005. "An analysis of the relative hardness of Reuters‐21578 subsets," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 56(6), pages 584-596, April.
- Mundaca, Luis & Markandya, Anil, 2016. "Assessing regional progress towards a ‘Green Energy Economy’," Applied Energy, Elsevier, vol. 179(C), pages 1372-1394.
- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
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More about this item
Keywords
textual analysis; Prior Adaptive Bayes classifier; Recovery and Resilience Plans; Sustainable Development Goals; pro-environment policy;All these keywords.
JEL classification:
- C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
- H22 - Public Economics - - Taxation, Subsidies, and Revenue - - - Incidence
- O44 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Environment and Growth
NEP fields
This paper has been announced in the following NEP Reports:- NEP-EEC-2024-02-19 (European Economics)
- NEP-ENV-2024-02-19 (Environmental Economics)
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