Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/253502 
Year of Publication: 
2021
Citation: 
[Journal:] Theoretical Economics [ISSN:] 1555-7561 [Volume:] 16 [Issue:] 1 [Publisher:] The Econometric Society [Place:] New Haven, CT [Year:] 2021 [Pages:] 49-71
Publisher: 
The Econometric Society, New Haven, CT
Abstract: 
We model an individual who wants to learn about a state of the world. The individual has a prior belief, and has data which consists of multiple forecasts about the state of the world. Our key assumption is that the decision maker identifies explanations that could have generated this data and among these focuses on the ones that maximise the likelihood of observing the data. The decision maker then bases her final prediction about the state on one of these maximum likelihood explanations. We show that in all the maximum likelihood explanations, moderate forecasts are just statistical derivatives of extreme ones. Therefore, the decision maker will base her final prediction only on the information conveyed in the relatively extreme forecasts. We show that this approach to combining forecasts leads to a unique prediction and a simple and dynamically consistent way of aggregating opinions.
Subjects: 
Maximum likelihood
combining forecasts
misspecified models
JEL: 
D8
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc Logo
Document Type: 
Article

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