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Comparative Environmental Economic Assessment

Edited by Raymond J.G.M. Florax, Peter Nijkamp and Kenneth G. Willis

Over the last decade, economists have increasingly recognized the role of meta-analysis and value transfer in synthesizing knowledge and efficiently exploiting the existing pool of knowledge. Comparative Environmental Economic Assessment explores the potential significance of using these techniques, particularly in environmental economics. Both meta-analysis and value transfer constitute major research tools which efficiently use knowledge previously acquired from other studies. The book focuses on the potential role and usefulness of these tools in environmental economic research, and goes on to address their validity, relevance and applicability
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Chapter 11: Meta-analysis: a Bayesian perspective

Chris Brunsdon and Kenneth G. Willis


Chris Brunsdon, Kenneth G. Willis 1 INTRODUCTION ‘Meta-analysis’ is an increasingly widely-used term to describe the process of integrating the findings of several research studies carried out in some area of interest. Typically, one wishes to combine several measurements of some parameter (possibly obtained using several different methods) to obtain an overall estimate, based on information from all of the previous studies. Additionally, one may wish to carry out statistical significance tests of some hypothesis relating to that parameter, again using information from all of the studies. It is hoped that the pooling of information will result in a parameter estimate with a smaller standard error or a more powerful significance test than that obtained from any one of the individual studies. This notion can be extended in a number of ways. If information about the methodology of each of the studies is available, one can perform a meta-regression in which the parameter estimates from each study are regressed against methodology variables. In this way, parameter estimates from individual studies can be standardized for the effects of different measurement techniques. Similarly, one can regress the parameter estimates against contextual variables. For example, if each study involves estimating some economic index in a specific geographical location, one could regress index estimates against a set of environmental and demographic variables for each location. This allows one to predict the likely index estimates in further geographical locations, provided the same environmental and demographic information is available. The...

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