Abducting Economics.
Abduction is the process of generating and choosing models, hypotheses, and data analyzed in response to surprising findings. All good empirical economists abduct. Explanations usually evolve as studies evolve. The abductive approach challenges economists to step outside the framework of received no...
| Publicado en: | American Economic Review Vol. 107; no. 5; pp. 298 - 303 |
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| Autores principales: | , |
| Formato: | Artículo |
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American Economic Association
May2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=123048358&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 123048358 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00028282 AER jtl: American Economic Review issn: 00028282 maglogo: N pubinfo: dt: May2017 vid: 107 iid: 5 pid: 22 pub: American Economic Association artinfo: ui: 123048358 10.1257/aer.p20171118 ppf: 298 ppct: 5 formats: tig: atl: Abducting Economics. aug: au: Heckman, James J. Singer, Burton affil: Department of Economics, University of Chicago, 1126 East 59th Street, Chicago, IL 60637 (e-mail: ) Emerging Pathogens Institute, University of Florida, PO Box 100009, Gainesville, FL 32610 (e-mail: ) su: Inference (Logic) Abduction (Logic) Economics methodology Explanation Iterative refinement sug: subj: Inference (Logic) Abduction (Logic) Economics methodology Explanation Iterative refinement ab: Abduction is the process of generating and choosing models, hypotheses, and data analyzed in response to surprising findings. All good empirical economists abduct. Explanations usually evolve as studies evolve. The abductive approach challenges economists to step outside the framework of received notions about the 'identification problem' that rigidly separates the act of model and hypothesis creation from the act of inference from data. It asks the analyst to engage models and data in an iterative dynamic process, using multiple models and sources of data in a back and forth where both models and data are augmented as learning evolves. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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