Automated Discovery in EconometricsNo 1469, Cowles Foundation Discussion Papers from Cowles Foundation, Yale University Abstract: Our subject is the notion of automated discovery in econometrics. Advances in computer power, electronic communication, and data collection processes have all changed the way econometrics is conducted. These advances have helped to elevate the status of empirical research within the economics profession in recent years and they now open up new possibilities for empirical econometric practice. Of particular significance is the ability to build econometric models in an automated way according to an algorithm of decision rules that allow for (what we call here) heteroskedastic and autocorrelation robust (HAR) inference. Computerized search algorithms may be implemented to seek out suitable models, thousands of regressions and model evaluations may be performed in seconds, statistical inference may be automated according to the properties of the data, and policy decisions can be made and adjusted in real time with the arrival of new data. We discuss some aspects and implications of these exciting, emergent trends in econometrics. Keywords: Automation; discovery; HAC estimation; HAR inference; model building; online econometrics; policy analysis; prediction; trends (search for similar items in EconPapers) Published in Econometric Theory (2005), 21(1) :3-20 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: http://EconPapers.repec.org/RePEc:cwl:cwldpp:1469 Ordering information: This working paper can be ordered from Access Statistics for this paper More papers in Cowles Foundation Discussion Papers from Cowles Foundation, Yale University |
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