In this paper we implement an effcient non-parametric statistical method, Random survival forests, for the selection of the determinants of Central Bank Independence (CBI) among a large data base of political and economic variables for OECD countries.This statistical technique enables us to overcome omitted variables and overftting problems. It turns out that the economic variables are major determinants compared to the political ones and linear andnonlinear effects of chosen predictors on CBI are found.
Cavicchioli, M., A., Papana, A., Papana Diagasis e B., Pistoresi. "Determinants of Central Bank independence: a random forest approach" Working paper, DEMB WORKING PAPER SERIES, Dipartimento di Economia Marco Biagi - Università di Modena e Reggio Emilia, 2016. https://doi.org/10.25431/11380_1101348
Determinants of Central Bank independence: a random forest approach
Cavicchioli, M.;Pistoresi, B.
2016
Abstract
In this paper we implement an effcient non-parametric statistical method, Random survival forests, for the selection of the determinants of Central Bank Independence (CBI) among a large data base of political and economic variables for OECD countries.This statistical technique enables us to overcome omitted variables and overftting problems. It turns out that the economic variables are major determinants compared to the political ones and linear andnonlinear effects of chosen predictors on CBI are found.
| File | Dimensione | Formato | |
|---|---|---|---|
|
0086.pdf
accesso aperto
Tipologia:
Versione dell'editore (versione pubblicata)
Dimensione
447.06 kB
Formato
Adobe PDF
|
447.06 kB | Adobe PDF | Visualizza/Apri |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris
