A life-cycle model with ambiguous survival beliefs
- On average, "young" people underestimate whereas "old" people overestimate their chances to survive into the future. We adopt a Bayesian learning model of ambiguous survival beliefs which replicates these patterns. The model is embedded within a non-expected utility model of life-cycle consumption and saving. Our analysis shows that agents with ambiguous survival beliefs (i) save less than originally planned, (ii) exhibit undersaving at younger ages, and (iii) hold larger amounts of assets in old age than their rational expectations counterparts who correctly assess their survival probabilities. Our ambiguity-driven model therefore simultaneously accounts for three important empirical findings on household saving behavior.
Author: | Max Groneck, Alexander LudwigORCiDGND, Alexander Zimper |
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URN: | urn:nbn:de:hebis:30:3-350177 |
DOI: | https://doi.org/10.2139/ssrn.2515703 |
Parent Title (English): | SAFE working paper series ; No. 73 |
Series (Serial Number): | SAFE working paper (73) |
Publisher: | SAFE |
Place of publication: | Frankfurt am Main |
Document Type: | Working Paper |
Language: | English |
Year of Completion: | 2014 |
Year of first Publication: | 2014 |
Publishing Institution: | Universitätsbibliothek Johann Christian Senckenberg |
Release Date: | 2014/11/03 |
Tag: | Choquet expected utility; Cumulative prospect theory; Dynamic inconsistency; Life-cycle hypothesis; Saving puzzles |
Issue: | October 20, 2014 |
Page Number: | 69 |
HeBIS-PPN: | 351017259 |
Institutes: | Wirtschaftswissenschaften / Wirtschaftswissenschaften |
Wissenschaftliche Zentren und koordinierte Programme / House of Finance (HoF) | |
Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS) | |
Wissenschaftliche Zentren und koordinierte Programme / Sustainable Architecture for Finance in Europe (SAFE) | |
Dewey Decimal Classification: | 3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft |
Sammlungen: | Universitätspublikationen |
Licence (German): | ![]() |