A random forests approach to assess determinants of central bank independence
Journal of Modern Applied Statistical Methods, ISSN: 1538-9472, Vol: 17, Issue: 2
2018
- 1Citations
- 820Usage
- 13Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
- Citations1
- Citation Indexes1
- Usage820
- Downloads607
- Abstract Views213
- Captures13
- Readers13
Article Description
A non-parametric efficient statistical method, Random Forests, is implemented for the selection of the determinants of Central Bank Independence (CBI) among a large database of economic, political, and institutional variables for OECD countries. It permits ranking all the determinants based on their importance in respect to the CBI and does not impose a priori assumptions on potential nonlinear relationships in the data. Collinearity issues are resolved, because correlated variables can be simultaneously considered.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85063725368&origin=inward; http://dx.doi.org/10.22237/jmasm/1553610953; https://jmasm.com/index.php/jmasm/article/view/997; https://digitalcommons.wayne.edu/jmasm/vol17/iss2/12; https://digitalcommons.wayne.edu/cgi/viewcontent.cgi?article=2611&context=jmasm; https://dx.doi.org/10.22237/jmasm/1553610953
The Netherlands Press
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