Data/hora: 10/06/2024 12:10:40

Provedor de dados: | 31
País: | United States
Título: | MODEL SELECTION CRITERIA USING LIKELIHOOD FUNCTIONS AND OUT-OF-SAMPLE PERFORMANCE
Autores: | Norwood,   F. Bailey; Ferrier,   Peyton Michael; Lusk,   Jayson L.
Data: | 2001-06-13
Ano: | 2001
Palavras-chave: | Research Methods/ Statistical Methods.
Resumo: | Model selection is often conducted by ranking models by their out-of-sample forecast error. Such criteria only incorporate information about the expected value, whereas models usually describe the entire probability distribution. Hence, researchers may desire a criteria evaluating the performance of the entire probability distribution. Such a method is proposed and is found to increase the likelihood of selecting the true model relative to conventional model ranking techniques.
Tipo: | Conference Paper or Presentation
Idioma: | Inglês

Identificador:

2861

http://purl.umn.edu/18947

Editor: | AgEcon Search

Relação:

NCR-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management&gt;2001 Conference, St. Louis, MO, April 23-24, 2001

2001 Conference, St. Louis, MO, April 23-24, 2001

Formato:

20

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