MODEL SELECTION CRITERIA USING LIKELIHOOD FUNCTIONS AND OUT-OF-SAMPLE PERFORMANCE Report as inadecuate




MODEL SELECTION CRITERIA USING LIKELIHOOD FUNCTIONS AND OUT-OF-SAMPLE PERFORMANCE - Download this document for free, or read online. Document in PDF available to download.

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.

Subject(s): Research Methods/ Statistical Methods

Issue Date: 2001

Publication Type: Conference Paper/ Presentation

PURL Identifier: http://purl.umn.edu/18947

Total Pages: 20

Series Statement: 2001 Conference, St. Louis, MO, April 23-24, 2001

Record appears in: Regional Research Projects > NCR-134/ NCCC-134 Applied Commodity Price Analysis, Forecasting, and Market Risk Management > 2001 Conference, April 23-24, 2001, St. Louis, Missouri





Author: Norwood, F. Bailey ; Ferrier, Peyton Michael ; Lusk, Jayson L.

Source: http://ageconsearch.umn.edu/record/18947?ln=en



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