TY - JOUR
T1 - A random forests approach to assess determinants of central bank independence
AU - Cavicchioli, Maddalena
AU - Papana, Ariadni
AU - Dagiasis, Ariadni Papana
AU - Pistoresi, Barbara
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Central bank independence
KW - Collinearity
KW - Determinants
KW - Minimal depth
KW - Random forests
UR - https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85063725368&origin=inward
UR - https://www.scopus.com/inward/citedby.uri?partnerID=HzOxMe3b&scp=85063725368&origin=inward
U2 - 10.22237/jmasm/1553610953
DO - 10.22237/jmasm/1553610953
M3 - Article
SN - 1538-9472
VL - 17
JO - Journal of Modern Applied Statistical Methods
JF - Journal of Modern Applied Statistical Methods
IS - 2
M1 - eP2611
ER -