dc.contributor.author |
Phillips, P.C.B. |
en |
dc.date.accessioned |
2009-09-07T01:20:59Z |
en |
dc.date.available |
2009-09-07T01:20:59Z |
en |
dc.date.issued |
2005 |
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dc.identifier.citation |
Econometric Theory 21 (1), 3-20. 2005 |
en |
dc.identifier.issn |
0266-4666 |
en |
dc.identifier.other |
eid=2-s2.0-15744405732 |
en |
dc.identifier.uri |
http://hdl.handle.net/2292/5213 |
en |
dc.description |
An open access copy of this article is available and complies with the copyright holder/publisher conditions. |
en |
dc.description.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. |
en |
dc.publisher |
Cambridge University Press |
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dc.relation.ispartofseries |
Econometric Theory |
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dc.rights |
Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/0266-4666/ |
en |
dc.rights.uri |
https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm |
en |
dc.source.uri |
http://dx.doi.org/10.1017/S0266466605050024 |
en |
dc.title |
Automated discovery in econometrics |
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dc.type |
Journal Article |
en |
dc.identifier.doi |
10.1017/S0266466605050024 |
en |
pubs.issue |
1 |
en |
pubs.begin-page |
3 |
en |
pubs.volume |
21 |
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dc.description.version |
VoR - Version of Record |
en |
dc.rights.holder |
Copyright: Cambridge University Press. |
en |
pubs.end-page |
20 |
en |
dc.rights.accessrights |
http://purl.org/eprint/accessRights/OpenAccess |
en |