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Is more data better?

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Abstract

Conventional 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 make better predictions by discarding old data if their model is mis-specified. The applicability of the results to some economic models is also demonstrated.

Suggested Citation

  • Kaushik Mitra, 2004. "Is more data better?," Royal Holloway, University of London: Discussion Papers in Economics 04/19, Department of Economics, Royal Holloway University of London, revised Jul 2004.
    • Handle: RePEc:hol:holodi:0419
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    File URL: http://www.rhul.ac.uk/economics/Research/WorkingPapers/pdf/dpe0419.pdf
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    Cited by:

    1. is not listed on IDEAS
    2. LeBaron, Blake, 2012. "Heterogeneous gain learning and the dynamics of asset prices," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 424-445.
    3. 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.
    4. 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.
    5. repec:isu:genstf:201501010800005596 is not listed on IDEAS
    6. 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.
    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. 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.
    9. 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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    Keywords

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    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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