In this paper we implement an efficient 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 overfitting problems. It turns out that the economic variables are major determinants compared to the political ones and linear and nonlinear effects of chosen predictors on CBI are found.
A Random Forests Approach to Assess Determinants of Central Bank Independence / Cavicchioli, Maddalena; Papana, Angeliki; Papana Dagiasis, Ariadni; Pistoresi, Barbara. - In: JOURNAL OF MODERN APPLIED STATISTICAL METHODS. - ISSN 1538-9472. - 17:2(2018), pp. 1-21. [10.22237/jmasm/1553610953]
A Random Forests Approach to Assess Determinants of Central Bank Independence
Maddalena Cavicchioli;Barbara Pistoresi
2018
Abstract
In this paper we implement an efficient 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 overfitting problems. It turns out that the economic variables are major determinants compared to the political ones and linear and nonlinear effects of chosen predictors on CBI are found.
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