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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)
ID
González Garibay, Montserrat
(
Avtor
),
ID
Srakar, Andrej
(
Avtor
),
ID
Bartolj, Tjaša
(
Avtor
),
ID
Sambt, Jože
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(2,29 MB)
MD5: DE848216F154C7BC7C4FEDE90CF88045
URL - Izvorni URL, za dostop obiščite
https://www.mdpi.com/2227-7390/10/1/152
Galerija slik
Izvleček
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
Število ogledov:
853
Število prenosov:
183
Metapodatki:
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Objavi na:
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
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