Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/284778 
Year of Publication: 
2021
Citation: 
[Journal:] Journal of Applied Econometrics [ISSN:] 1099-1255 [Volume:] 36 [Issue:] 6 [Year:] 2021 [Pages:] 728-743
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
Point forecasts can be interpreted as functionals (i.e., point summaries) of predictive distributions. We extend methodology for the identification of the functional based on time series of point forecasts and associated realizations. Focusing on state‐dependent quantiles and expectiles, we provide a generalized method of moments estimator for the functional, along with tests of optimality under general joint hypotheses of functional relationships and information bases. Our tests are more flexible, and in simulations better calibrated and more powerful than existing solutions. In empirical examples, economic growth forecasts and model output for precipitation are indicative of overstatement in anticipation of extreme events.
Subjects: 
expectile
identifying moment conditions
information set
loss function
optimality of point forecasts
quantile
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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