20.500.12556/RUL-134971 Does machine learning offer added value vis-à-vis traditional statistics? an exploratory study on retirement decisions using data from the Survey of Health, Ageing, and Retirement in Europe (SHARE) Do machine learning algorithms perform better than statistical survival analysis when predicting retirement decisions? This exploratory article addresses the question by constructing a pseudo-panel with retirement data from the Survey of Health, Ageing, and Retirement in Europe (SHARE). The analysis consists of two methodological steps prompted by the nature of the data. First, a discrete Cox survival model of transitions to retirement with time-dependent covariates is compared to a Cox model without time-dependent covariates and a survival random forest. Second, the best performing model (Cox with time-dependent covariates) is compared to random forests adapted to time-dependent covariates by means of simulations. The results from the analysis do not clearly favor a single method; whereas machine learning algorithms have a stronger predictive power, the variables they use in their predictions do not necessarily display causal relationships with the outcome variable. Therefore, the two methods should be seen as complements rather than substitutes. In addition, simulations shed a new light on the role of some variables—such as education and health—in retirement decisions. This amounts to both substantive and methodological contributions to the literature on the modeling of retirement. statistics machine learning random forests survival analysis retirement time-dependent covariates statistika strojno učenje naključni gozdovi analiza preživetja upokojitev true false true Angleški jezik Slovenski jezik Članek v reviji 2022-02-15 14:31:00 2022-02-15 14:40:42 2022-09-14 04:10:56 0000-00-00 00:00:00 2022 0 0 25 str. iss. 1, art. 152 Vol. 10 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.8:311 2227-7390 10.3390/math10010152 92089347 RAZ_Gonzalez_Garibay_Montserrat_2022.pdf RAZ_Gonzalez_Garibay_Montserrat_2022.pdf 1 DE848216F154C7BC7C4FEDE90CF88045 18bbeb329503bf3f61a97165b02e54e1c0529c995ecdce6c7d6298b4cc2e606a 8b192ec9-8e64-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=153766 https://www.mdpi.com/2227-7390/10/1/152 0 923ca332-8e64-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=153767 Ekonomska fakulteta 0 0 0