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Learning about ambiguous long-term prospects

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  • Choi, Hongseok

Abstract

This paper investigates whether ambiguity afflicting the long-run rate of growth fades away in a nonexchangeable environment (time-varying instantaneous expected growth rate). Two types of ambiguity are considered: static (multiple priors) and dynamic (multiple laws of motion). In the absence of dynamic ambiguity, likelihood-based learning resolves static ambiguity. In the presence of dynamic ambiguity, on the other hand, likelihood-based learning fails. In this case, static ambiguity fades away if the agent incorporates into the objective criteria (likelihood) her subjective criteria (penalty proportional to the Kullback–Leibler divergence). The model of learning is also applied to portfolio choice.

Suggested Citation

  • Choi, Hongseok, 2026. "Learning about ambiguous long-term prospects," Journal of Economic Theory, Elsevier, vol. 233(C).
    • Handle: RePEc:eee:jetheo:v:233:y:2026:i:c:s0022053126000116
    DOI: 10.1016/j.jet.2026.106147
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