Compositional combination and selection of forecasters
The Split-Then-Combine approach has previously been used to generate the weights of forecasts in a combination in the Euclidean space. This paper extends this approach to combine forecasts inside the simplex space, the sample space of positive weights adding up to one. As it turns out, the simplicia...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/397838 |
| Acceso en línea: | https://hdl.handle.net/2117/397838 https://dx.doi.org/10.2436/20.8080.02.123 |
| Access Level: | acceso abierto |
| Palabra clave: | Mathematical economics Topology Mathematical statistics Aitchison geometry combination-after-selection dimensionality problem simplex split-then-combine 91B Matemàtica financera 54C Aplicacions i tipus generals d'espais definits per aplicacions 62P Aplicacions Classificació AMS::91 Game theory, economics, social and behavioral sciences::91B Mathematical economics Classificació AMS::54 General topology::54C Maps and general types of spaces defined by maps Classificació AMS::62 Statistics::62P Applications Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica |
| Sumario: | The Split-Then-Combine approach has previously been used to generate the weights of forecasts in a combination in the Euclidean space. This paper extends this approach to combine forecasts inside the simplex space, the sample space of positive weights adding up to one. As it turns out, the simplicial statistic given by the sample centre compares favourably against the fixed-weight, average forecast. Besides, we also develop a Combination-After-Selection method to get rid of redundant forecasters. We apply these approaches to make out-of-sample one-step ahead combinations and subcombinations of forecasts for several economic variables. This methodology is particularly useful when the sample size is smaller than the number of forecasts, a case where other methods (e.g., ordinary least squares or principal component analysis) are not applicable. |
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