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Facts and Artifacts: Calibration and the Empirical Assessment of Real-Business-Cycle Models

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  • Hoover, Kevin D

Abstract

Advocates of real-business-cycle models typically hold calibration methods to be superior to econometric estimation as means of quantifying the models for policy analysis. This paper finds a coherent foundation for calibration methods in Herbert Simon's Sciences of the Artificial and the methodology of idealization in the philosophy of science. Although coherent, these foundations are not fundamentally connected real-business-cycle models per se. Furthermore, adequate comparative standards have not yet been developed for calibrated models. Copyright 1995 by Royal Economic Society.

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Bibliographic Info

Article provided by Oxford University Press in its journal Oxford Economic Papers.

Volume (Year): 47 (1995)
Issue (Month): 1 (January)
Pages: 24-44

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Handle: RePEc:oup:oxecpp:v:47:y:1995:i:1:p:24-44

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Cited by:
  1. Shamik Dhar & Stephen P Millard, 2000. "A limited participation model of the monetary transmission mechanism in the United Kingdom," Bank of England working papers 117, Bank of England.
  2. Bennett T. McCallum, 1999. "Recent Developments in Monetary Policy Analysis: The Roles of Theory and Evidence," NBER Working Papers 7088, National Bureau of Economic Research, Inc.
  3. Prof John Foster, 2007. "A micro-meso-macro perspective on the methodology of evolutionary economics: integrating history, simulation and econometrics," Discussion Papers Series 343, School of Economics, University of Queensland, Australia.
  4. Cozzi, Marco, 2014. "Equilibrium Heterogeneous-Agent models as measurement tools: Some Monte Carlo evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 39(C), pages 208-226.
  5. Engsted, Tom, 2002. " Measures of Fit for Rational Expectations Models," Journal of Economic Surveys, Wiley Blackwell, vol. 16(3), pages 301-55, July.
  6. Roger E. Backhouse & Steven N. Durlauf, 2009. "Robbins on Economic Generalizations and Reality in the Light of Modern Econometrics," Economica, London School of Economics and Political Science, vol. 76(s1), pages 873-890, October.
  7. Giorgio Fagiolo & Paul Windrum & Alessio Moneta, 2006. "Empirical Validation of Agent Based Models: A Critical Survey," LEM Papers Series 2006/14, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  8. Dridi, Ramdan & Guay, Alain & Renault, Eric, 2007. "Indirect inference and calibration of dynamic stochastic general equilibrium models," Journal of Econometrics, Elsevier, vol. 136(2), pages 397-430, February.
  9. Celsa Machado, 2001. "Measuring Business Cycles: The Real Business Cycle Approach and Related Controversies," FEP Working Papers 107, Universidade do Porto, Faculdade de Economia do Porto.
  10. SENBATA, Sisay Regassa, 2011. "How applicable are the new Keynesian DSGE models to a typical low-income economy?," Working Papers 2011016, University of Antwerp, Faculty of Applied Economics.
  11. Budzinski, Oliver & Christiansen, Arndt, 2007. "The Oracle/PeopleSoft case: unilateral effects, simulation models and econometrics in contemporary merger control," IBES Diskussionsbeiträge 157, University of Duisburg-Essen, Faculty for Economics and Business Administration.
  12. Pedro Garcia Duarte & Kevin D. Hoover, 2012. "Observing Shocks," History of Political Economy, Duke University Press, vol. 44(5), pages 226-249, Supplemen.
  13. Brevik, Frode & d'Addona, Stefano, 2013. "Is Ignorance Bliss? The Cost Of Business-Cycle Uncertainty," Macroeconomic Dynamics, Cambridge University Press, vol. 17(04), pages 728-746, June.
  14. Ossama Mikhail, 2004. "No More Rocking Horses: Trading Business-Cycle Depth for Duration Using an Economy-Specific Characteristic," Macroeconomics 0402026, EconWPA.
  15. Kakarot-Handtke, Egmont, 2012. "The rhetoric of failure: a hyper-dialog about method in economics and how to get things going," MPRA Paper 43276, University Library of Munich, Germany.

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