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Automated Discovery in Econometrics

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.

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Paper provided by Cowles Foundation for Research in Economics, Yale University in its series Cowles Foundation Discussion Papers with number 1469.

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Length: 21 pages
Date of creation: Jul 2004
Date of revision:
Publication status: Published in Econometric Theory (2005), 21(1) :3-20
Handle: RePEc:cwl:cwldpp:1469
Note: CFP 1149.
Contact details of provider: Postal: Yale University, Box 208281, New Haven, CT 06520-8281 USA
Phone: (203) 432-3702
Fax: (203) 432-6167
Web page:

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Order Information: Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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  8. Oliver Linton, 2004. "Nonparametric inference for unbalanced time series data," LSE Research Online Documents on Economics 2116, London School of Economics and Political Science, LSE Library.
  9. Hannes Leeb & Benedikt M. Potscher, 2003. "Can One Estimate the Conditional Distribution of Post-Model-Selection Estimators?," Cowles Foundation Discussion Papers 1444, Cowles Foundation for Research in Economics, Yale University.
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  12. Duchesne, Pierre, 2006. "On Testing For Serial Correlation With A Wavelet-Based Spectral Density Estimator In Multivariate Time Series," Econometric Theory, Cambridge University Press, vol. 22(04), pages 633-676, August.
  13. Pesaran, M Hashem & Timmermann, Allan, 2000. "A Recursive Modelling Approach to Predicting UK Stock Returns," Economic Journal, Royal Economic Society, vol. 110(460), pages 159-91, January.
  14. Kiefer, Nicholas M. & Bunzel, Helle & Vogelsang, Timothy & Vogelsang, Timothy & Bunzel, Helle, 2000. "Simple Robust Testing of Regression Hypotheses," Staff General Research Papers 1832, Iowa State University, Department of Economics.
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  19. Dietmar Bauer & Martin Wagner, 2003. "A Canonical Form for Unit Root Processes in the State Space Framework," Diskussionsschriften dp0312, Universitaet Bern, Departement Volkswirtschaft.
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  24. Halbert White, 2000. "A Reality Check for Data Snooping," Econometrica, Econometric Society, vol. 68(5), pages 1097-1126, September.
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  35. Peter C.B. Phillips, 1992. "Bayes Methods for Trending Multiple Time Series with an Empirical Application to the US Economy," Cowles Foundation Discussion Papers 1025, Cowles Foundation for Research in Economics, Yale University.
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  37. Tony Lancaster, 2006. "A note on bootstraps and robustness," CeMMAP working papers CWP04/06, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
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  39. Danilov, Dmitry & Magnus, J.R.Jan R., 2004. "On the harm that ignoring pretesting can cause," Journal of Econometrics, Elsevier, vol. 122(1), pages 27-46, September.
  40. Kuersteiner, Guido M., 2005. "Automatic Inference For Infinite Order Vector Autoregressions," Econometric Theory, Cambridge University Press, vol. 21(01), pages 85-115, February.
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