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Is more data better?

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  • Kaushik Mitra

Abstract

Conventional wisdom usually suggests that agents should use all the data they have to make the best possible prediction. In this paper, however, it is shown that agents may sometimes be able to make better predictions by throwing away old data. The optimality criterion agents adopt is the mean squared error criterion.

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File URL: http://www.york.ac.uk/media/economics/documents/discussionpapers/2000/0044.pdf
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Paper provided by Department of Economics, University of York in its series Discussion Papers with number 00/44.

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Handle: RePEc:yor:yorken:00/44

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Keywords: mean squared error; prediction; optimality.;

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References

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  1. Kaushik Mitra & Seppo Honkapohja, 1999. "Learning with Bounded Memory in Stochastic Models," Computing in Economics and Finance 1999 221, Society for Computational Economics.
  2. Evans, George W. & Honkapohja, Seppo, 1999. "Learning dynamics," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 7, pages 449-542 Elsevier.
  3. Bray, Margaret, 1982. "Learning, estimation, and the stability of rational expectations," Journal of Economic Theory, Elsevier, vol. 26(2), pages 318-339, April.
  4. Lucas, Robert E, Jr, 1973. "Some International Evidence on Output-Inflation Tradeoffs," American Economic Review, American Economic Association, vol. 63(3), pages 326-34, June.
  5. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
  6. Hommes, Cars H., 1998. "On the consistency of backward-looking expectations: The case of the cobweb," Journal of Economic Behavior & Organization, Elsevier, vol. 33(3-4), pages 333-362, January.
  7. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
  8. repec:cup:macdyn:v:2:y:1998:i:3:p:287-321 is not listed on IDEAS
  9. Evans, G B A & Savin, N E, 1984. "Testing for Unit Roots: 2," Econometrica, Econometric Society, vol. 52(5), pages 1241-69, September.
  10. Hommes, Cars & Sorger, Gerhard, 1998. "Consistent Expectations Equilibria," Macroeconomic Dynamics, Cambridge University Press, vol. 2(03), pages 287-321, September.
  11. Evans, G B A & Savin, N E, 1981. "Testing for Unit Roots: 1," Econometrica, Econometric Society, vol. 49(3), pages 753-79, May.
  12. George W. Evans & Seppo Honkapohja, 1993. "Adaptive forecasts, hysteresis, and endogenous fluctuations," Economic Review, Federal Reserve Bank of San Francisco, pages 3-13.
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Cited by:
  1. LeBaron, Blake, 2012. "Heterogeneous gain learning and the dynamics of asset prices," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 424-445.
  2. Kaushik Mitra & Seppo Honkapohja, 1999. "Learning with Bounded Memory in Stochastic Models," Computing in Economics and Finance 1999 221, Society for Computational Economics.
  3. Blake LeBaron, 2010. "Heterogeneous Gain Learning and Long Swings in Asset Prices," Working Papers 10, Brandeis University, Department of Economics and International Businesss School.
  4. Pyo, Dong-Jin, 2014. "A Multi-Factor Model of Heterogeneous Traders in a Dynamic Stock Market," Staff General Research Papers 37358, Iowa State University, Department of Economics.
  5. LeBaron, Blake, 2006. "Agent-based Computational Finance," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 24, pages 1187-1233 Elsevier.

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