Combining brain and behavioral data to improve econometric policy analysis
Daniel Houser ()
No 1037, 2007 Meeting Papers from Society for Economic Dynamics
From brain imaging data one obtains new economic theory with economically relevant policy implications. In particular, one would like to use data from a small number of subjects in an imaging experiment to predict a larger population’s response to proposed policy changes. This paper develops a method by which one can combine behavioral and imaging experiments’ data to provide empirical content to economic theory and improve econometric policy analysis. I develop probability bounds on the behavioral effects of a policy change, and show that these bounds depend on the probability that certain brain activation patterns are present. Moreover, I show that these activation probabilities can be estimated from a combination of behavioral and imaging experiments, so long as decisions in the behavioral experiment are sufficiently dependent on the activation patterns of interest.
References: Add references at CitEc
Citations Track citations by RSS feed
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: http://EconPapers.repec.org/RePEc:red:sed007:1037
Access Statistics for this paper
More papers in 2007 Meeting Papers from Society for Economic Dynamics
Address: Society for Economic Dynamics Christian Zimmermann Economic Research Federal Reserve Bank of St. Louis PO Box 442 St. Louis MO 63166-0442 USA
Contact information at EDIRC.
Series data maintained by Christian Zimmermann ().