Meta-Regression and Benefit Transfer: Data Space, Model Space, and the Quest for ‘Optimal Scope’
Meta-functional Benefit Transfer, while conceptually attractive, is often plagued by the paucity of available source studies and related small sample problems. A broadening of scope of the Meta-Regression Model by adding data from “related, yet different” contexts or activities may circumvent these issues, but may not necessarily enhance the efficiency of transfer functions if the different contexts do not share policy-relevant parameters. We illustrate how different combinations of contexts can be interpreted as ‘data spaces’ which can then be explored for the most promising transfer function using Bayesian Model Search techniques. Our results indicate that for some scope-augmented data spaces model-averaged benefit predictions can be more efficient than those flowing from the baseline context and data.
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