Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/314221 
Year of Publication: 
2023
Citation: 
[Journal:] Journal of Applied Economics [ISSN:] 1667-6726 [Volume:] 26 [Issue:] 1 [Article No.:] 2185975 [Year:] 2023 [Pages:] 1-21
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
Survey forecasts are prone to entry and exit of forecasters as well as forecasters not contributing every period leading to gaps. These gaps make it difficult to compare individual forecasters to each other and raises the question of how to deal with the missing observations. This is addressed for the variables GDP, CPI inflation, and unemployment for the US. The theoretically optimal method of filling in missing observations is derived and compared to several competing methods. It is found that not filling in missing observations and taking the previous value do not perform particularly well. For the other methods assessed, there is no clear superior approach for all use cases, but the theoretically optimal one usually performs quite well.
Subjects: 
entry
exit
Gap
imputation
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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