Automated Discovery in Econometrics

Peter Phillips

No 1469, Cowles Foundation Discussion Papers from Cowles Foundation for Research in Economics, 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)
JEL-codes: C10 C32 C50 C87 (search for similar items in EconPapers)
Pages: 21 pages
Date: 2004-07
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
Note: CFP 1149.
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Published in Econometric Theory (2005), 21(1) :3-20

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Journal Article: AUTOMATED DISCOVERY IN ECONOMETRICS (2005)
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