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Distance and prediction error variance constraints for ARMA model portfolios
Poskitt and Tremayne 74 (1987) present a posterior odds ratio ( R ) portfolio selection strategy for ARMA models. This paper makes the range of prediction error variances that are implicit in R more explicit. Model closeness is quantified using a distance function in a Hilbert space. The relationshi...
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Published in: | International journal of forecasting 2004, Vol.20 (1), p.41-52 |
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container_end_page | 52 |
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container_start_page | 41 |
container_title | International journal of forecasting |
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creator | Chenoweth, Timothy Dowling, Karen Hubata, Robert St. Louis, Robert |
description | Poskitt and Tremayne 74 (1987) present a posterior odds ratio (
R
) portfolio selection strategy for ARMA models. This paper makes the range of prediction error variances that are implicit in
R
more explicit. Model closeness is quantified using a distance function in a Hilbert space. The relationship between distance and the posterior odds ratio is demonstrated. This provides a distance interpretation of the posterior odds ratio. The distance function also makes it possible to develop a prediction error variance (p.e.v.) criterion for identifying models to include in an ARMA model portfolio. A simulation experiment shows that the p.e.v. criterion provides forecasters with both a measure for assessing the likelihood that the models in an ARMA model portfolio yield practically equivalent forecasts, and a measure for assessing the usefulness of alternative criteria for identifying the order of an ARMA model. |
doi_str_mv | 10.1016/S0169-2070(03)00006-2 |
format | article |
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R
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R
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R
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R
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R
) portfolio selection strategy for ARMA models. This paper makes the range of prediction error variances that are implicit in
R
more explicit. Model closeness is quantified using a distance function in a Hilbert space. The relationship between distance and the posterior odds ratio is demonstrated. This provides a distance interpretation of the posterior odds ratio. The distance function also makes it possible to develop a prediction error variance (p.e.v.) criterion for identifying models to include in an ARMA model portfolio. A simulation experiment shows that the p.e.v. criterion provides forecasters with both a measure for assessing the likelihood that the models in an ARMA model portfolio yield practically equivalent forecasts, and a measure for assessing the usefulness of alternative criteria for identifying the order of an ARMA model.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/S0169-2070(03)00006-2</doi><tpages>12</tpages></addata></record> |
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subjects | Distance Forecasting techniques Information criteria Misspecification error Order determination Posterior odds ratio Prediction error variance Studies |
title | Distance and prediction error variance constraints for ARMA model portfolios |
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