In this paper we implement an e cient non-parametric statistical method, Random survival forests, for the selection of the determinants of Central Bank Independence (CBI) among a large database of political and economic variables for OECD countries. This statistical technique enables us to overcome omitted variables and over tting problems. It turns out that the economic variables are major determinants compared to the political ones and linear and nonlinear e ects of chosen predictors on CBI are found.
Cavicchioli, M., A., Papana, A., Papana Dagiasis e B., Pistoresi. "Determinants of Central Bank Independence: a Random Forest Approach" Working paper, RECENT WORKING PAPER SERIES, Dipartimento di Economia Marco Biagi – Università di Modena e Reggio Emilia, 2016.
Determinants of Central Bank Independence: a Random Forest Approach
Cavicchioli, M.;Pistoresi, B.
2016
Abstract
In this paper we implement an e cient non-parametric statistical method, Random survival forests, for the selection of the determinants of Central Bank Independence (CBI) among a large database of political and economic variables for OECD countries. This statistical technique enables us to overcome omitted variables and over tting problems. It turns out that the economic variables are major determinants compared to the political ones and linear and nonlinear e ects of chosen predictors on CBI are found.
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