Identification with Models and Exogenous Data Variation
dc.contributor.author | Kahn, R. Jay | |
dc.contributor.author | Whited, Toni M. | |
dc.date.accessioned | 2016-07-14T12:31:31Z | |
dc.date.available | 2016-07-14T12:31:31Z | |
dc.date.issued | 2016-07 | |
dc.identifier | 1323 | en_US |
dc.identifier.citation | Forthcoming, Foundations and Trends in Accounting | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/122724 | |
dc.description.abstract | We distinguish between identification and establishing causality. Identification means forming a unique mapping from features of data to quantities that are of interest to economists. Establishing causality is synonymous with finding sources of exogenous variation. These two issues are often confused. However, exogenous variation is only sometimes necessary and never sufficient to identify economically interesting parameters. Instead, even for causal questions identification must rest on an underlying economic model. We illustrate these points by examining identification in two recent papers: one causal study relying on an entirely verbal model and one non-causal study relying on a formal mathematical model. | en_US |
dc.subject | Identification | en_US |
dc.subject | Causality | en_US |
dc.subject | Natural Experiments | en_US |
dc.subject | Structural Estimation | en_US |
dc.subject.classification | Finance | en_US |
dc.title | Identification with Models and Exogenous Data Variation | en_US |
dc.type | Working Paper | en_US |
dc.subject.hlbsecondlevel | Finance | en_US |
dc.subject.hlbtoplevel | Business | |
dc.contributor.affiliationum | Ross School of Business | en_US |
dc.contributor.affiliationumcampus | Ann Arbor | |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/122724/1/1323_Whited.pdf | |
dc.owningcollname | Business, Stephen M. Ross School of - Working Papers Series |
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