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Automated Discovery in Econometrics

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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.

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

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Keywords: Automation; discovery; HAC estimation; HAR inference; model building; online econometrics; policy analysis; prediction; trends;

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Cited by:
  1. Qin, Duo, 2007. "Uncover Latent PPP by Dynamic Factor Error Correction Model (DF-ECM) Approach: Evidence from five OECD countries," Economics Discussion Papers 2007-29, Kiel Institute for the World Economy.
  2. Dahl, Christian M. & Hansen, Henrik & Smidt, John, 2009. "The cyclical component factor model," International Journal of Forecasting, Elsevier, vol. 25(1), pages 119-127.
  3. Heij, C. & Groenen, P.J.F. & van Dijk, D.J.C., 2006. "Time series forecasting by principal covariate regression," Econometric Institute Research Papers EI 2006-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  4. Jennifer Castle & Xiaochuan Qin & W. Robert Reed, 2011. "Using Model Selection Algorthims to Obtain Reliable Coefficient Estimates," Working Papers in Economics 11/03, University of Canterbury, Department of Economics and Finance.
  5. Jane E. Ihrig & Mario Marazzi & Alexander D. Rothenberg, 2006. "Exchange-rate pass-through in the G-7 countries," International Finance Discussion Papers 851, Board of Governors of the Federal Reserve System (U.S.).
  6. Bernd Brandl & Christian Keber & Matthias Schuster, 2006. "An automated econometric decision support system: forecasts for foreign exchange trades," Central European Journal of Operations Research, Springer, vol. 14(4), pages 401-415, December.
  7. Marquez, Jaime, 2006. "Estimating elasticities for U.S. trade in services," Economic Modelling, Elsevier, vol. 23(2), pages 276-307, March.

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