Relative Maximum Likelihood updating of ambiguous beliefs

Cheng, Xiaoyu

doi:10.1016/j.jmateco.2021.102587

Cited by 12 publications

(3 citation statements)

References 26 publications

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“…In other words, objective payoff from the data-driven decision will be 1/3 from each user, strictly lower than what Netflix can get from simply ignoring the data and taking the data-free decision (1/2). The same conclusion also applies more generally to updating rules that based on maximum likelihood, such as relative maximum likelihood (Cheng, 2021) and likelihood-ratio updating (Epstein and Schneider, 2007).…”

Section: Illustrative Examplementioning

confidence: 80%

“…This paper also contributes to the literature on dynamic decisions under ambiguity by proposing new updating rules for sets of distributions. See Gilboa and Marinacci (2013) and Cheng (2021) for recent developments. The essential departure of the present paper from papers in this literature is that it evaluates decisions according to an objective criterion, i.e., expected utility under the true DGP.…”

Section: Related Literaturementioning

confidence: 99%

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Robust Data-Driven Decisions Under Model Uncertainty

Cheng¹

2022

Preprint

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When sample data are governed by an unknown sequence of independent but possibly non-identical distributions, the data-generating process (DGP) in general cannot be perfectly identified from the data. For making decisions facing such uncertainty, this paper presents a novel approach by studying how the data can best be used to robustly improve decisions.That is, no matter which DGP governs the uncertainty, one can make a better decision than without using the data. I show that common inference methods, e.g., maximum likelihood and Bayesian updating cannot achieve this goal. To address, I develop new updating rules that lead to robustly better decisions either asymptotically almost surely or in finite sample with a prespecified probability. Especially, they are easy to implement as are given by simple extensions of the standard statistical procedures in the case where the possible DGPs are all independent and identically distributed. Finally, I show that the new updating rules also lead to more intuitive conclusions in existing economic models such as asset pricing under ambiguity.

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Section: Illustrative Examplementioning

confidence: 80%

Section: Related Literaturementioning

confidence: 99%

Robust Data-Driven Decisions Under Model Uncertainty

Cheng¹

2022

Preprint

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“…In general, SEM assumes that the observational variable is a continuous variable that distributes a normal multivariate distribution [9].Thus this study analyzed the public Health Belief Model against COVID-19 vaccination, especially in Jambi City using SEM (Structural Equation Modeling) analysis. In this study, the estimation method used is Maximum Likelihood (ML) [14,15,16,17,18,19,20,21]. According to [22], ML is effective on the number of samples between 150-400 data.…”

Section: Introductionmentioning

confidence: 99%

Health Belief Model of Jambi City Community Against Covid-19 Vaccination with Structural Equation Modeling (SEM) Method

Yurinanda

Multahadah

Marisa

2022

Eksakta

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The community's HBM (Health Belief Model) analysis of the COVID-19 vaccination needs to be done. This perceived threat assessment is based on perceived vulnerability and seriousness. Judgments to behave in response to perceived threats are also influenced by cues to action. Variables that cannot be explained directly or variables that require explanation from other variables are called latent variables. Latent variables consist of exogenous and endogenous variables. This study aims to analyze the public health belief model of the city of Jambi towards COVID-19 vaccination with the structural equation modeling (SEM) method. The results showed that the health belief model for COVID-19 vaccination in Jambi City, vaccination actions were significantly influenced by perceptions of benefits and barriers. Perceived benefits and barriers were significantly affected by perceived severity and seriousness and then perceived severity and seriousness were significantly influenced by cues to action. However, demographics including age, occupation, income and beliefs in this study did not significantly influence a person to vaccinate. The proposed model can be accepted based on the goodness of fit indicator

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Ambiguity and partial Bayesian updating

Kovach

2023

Econ Theory

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Relative Maximum Likelihood updating of ambiguous beliefs

Cited by 12 publications

References 26 publications

Robust Data-Driven Decisions Under Model Uncertainty

Robust Data-Driven Decisions Under Model Uncertainty

Health Belief Model of Jambi City Community Against Covid-19 Vaccination with Structural Equation Modeling (SEM) Method

Ambiguity and partial Bayesian updating

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