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Is More Data Better?

Author

Listed:
  • Mitra, K.
Abstract
Convention wisdom usually suggests that agents should use all the data they have to make the best possible prediction. In this paper it is shown that agents may sometimes be able to make better predictions by throwing away data. The optimality criterion agents adopt is the mean squared criterion.

Suggested Citation

  • Mitra, K., 1999. "Is More Data Better?," University of Helsinki, Department of Economics 452, Department of Economics.
    • Handle: RePEc:fth:helsec:452
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    References listed on IDEAS

    as
    1. 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.
    2. 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 1057-1072, June.
    3. Honkapohja, Seppo & Mitra, Kaushik, 2003. "Learning with bounded memory in stochastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 27(8), pages 1437-1457, June.
    4. Evans, G B A & Savin, N E, 1984. "Testing for Unit Roots: 2," Econometrica, Econometric Society, vol. 52(5), pages 1241-1269, September.
    5. Bray, Margaret, 1982. "Learning, estimation, and the stability of rational expectations," Journal of Economic Theory, Elsevier, vol. 26(2), pages 318-339, April.
    6. 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.
    7. Lucas, Robert E, Jr, 1973. "Some International Evidence on Output-Inflation Tradeoffs," American Economic Review, American Economic Association, vol. 63(3), pages 326-334, June.
    8. Sargent, Thomas J., 1991. "Equilibrium with signal extraction from endogenous variables," Journal of Economic Dynamics and Control, Elsevier, vol. 15(2), pages 245-273, April.
    9. repec:cup:macdyn:v:2:y:1998:i:3:p:287-321 is not listed on IDEAS
    10. Hommes, Cars & Sorger, Gerhard, 1998. "Consistent Expectations Equilibria," Macroeconomic Dynamics, Cambridge University Press, vol. 2(3), pages 287-321, September.
    11. George W. Evans & Seppo Honkapohja, 1993. "Adaptive forecasts, hysteresis, and endogenous fluctuations," Economic Review, Federal Reserve Bank of San Francisco, pages 3-13.
    12. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    13. Evans, G B A & Savin, N E, 1981. "Testing for Unit Roots: 1," Econometrica, Econometric Society, vol. 49(3), pages 753-779, May.
    Full references (including those not matched with items on IDEAS)

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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. Pyo, Dong-Jin, 2014. "A Multi-Factor Model of Heterogeneous Traders in a Dynamic Stock Market," Staff General Research Papers Archive 37358, Iowa State University, Department of Economics.
    3. Honkapohja, Seppo & Mitra, Kaushik, 2003. "Learning with bounded memory in stochastic models," Journal of Economic Dynamics and Control, Elsevier, vol. 27(8), pages 1437-1457, June.
    4. Pyo, Dong-Jin, 2015. "Animal spirits and stock market dynamics," ISU General Staff Papers 201501010800005596, Iowa State University, Department of Economics.
    5. Dong-Jin Pyo, 2017. "A multi-factor model of heterogeneous traders in a dynamic stock market," Cogent Economics & Finance, Taylor & Francis Journals, vol. 5(1), pages 1416902-141, January.
    6. Chiarella, Carl & He, Xue-Zhong & Zwinkels, Remco C.J., 2014. "Heterogeneous expectations in asset pricing: Empirical evidence from the S&P500," Journal of Economic Behavior & Organization, Elsevier, vol. 105(C), pages 1-16.
    7. Blake LeBaron, 2010. "Heterogeneous Gain Learning and Long Swings in Asset Prices," Working Papers 10, Brandeis University, Department of Economics and International Business School.
    8. 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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    More about this item

    Keywords

    TESTING ; SIMULATION ; PROJECTIONS ; INFORMATION ; PRICES;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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