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Predicting Resource Policy Outcomes via Meta-Regression: Data Space, Model Space, and the Quest for 'Optimal Scope'

Listed author(s):
  • Moeltner Klaus

    ()

    (University of Nevada - Reno)

  • Rosenberger Randall S

    ()

    (Oregon State University)

Resource-managing agencies are increasingly relying on secondary data to predict economic benefits for planned policy interventions. This `transfer of benefits' is often based on a quantitative synthesis of aggregate results for similar past interventions via Meta-Regression Models. However, this approach is generally plagued by the paucity of available 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 model-averaged benefit predictions for scope-augmented data spaces can be more robust and efficient than those flowing from the baseline context and data.

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Article provided by De Gruyter in its journal The B.E. Journal of Economic Analysis & Policy.

Volume (Year): 8 (2008)
Issue (Month): 1 (August)
Pages: 1-31

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Handle: RePEc:bpj:bejeap:v:8:y:2008:i:1:n:31
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