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)

González Garibay, Montserrat (avtor), Srakar, Andrej (avtor), Bartolj, Tjaša (avtor), Sambt, Jože (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: statistics, machine learning, random forests, survival analysis, retirement, time-dependent covariates
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: EF - Ekonomska fakulteta
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2022
• Št. strani: 25 str.
• Številčenje: Vol. 10, iss. 1, art. 152
• PID: 20.500.12556/RUL-134971
• UDK: 004.8:311
• ISSN pri članku: 2227-7390
• DOI: 10.3390/math10010152
• COBISS.SI-ID: 92089347
• Datum objave v RUL: 15.02.2022

Gradivo je del revije

• Naslov: Mathematics
• Skrajšan naslov: Mathematics
• Založnik: MDPI AG
• ISSN: 2227-7390
• COBISS.SI-ID: 523267865

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
• Začetek licenciranja: 04.01.2022

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: statistika, strojno učenje, naključni gozdovi, analiza preživetja, upokojitev

Projekti

• Financer: EC - European Commission
• Program financ.: European Social Fund
• Naslov: Upgrade of Analytical Models Concerning the Pension Scheme

• Financer: Drugi - Drug financer ali več financerjev
• Program financ.: Slovenia, Ministry of Labour, Family, Social Affairs, and Equal Opportunities
• Naslov: Upgrade of Analytical Models Concerning the Pension Scheme