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Asymptotic Principal Components Estimation of Large Factor Models


  • Victor Solo
  • Chris Heaton


There has been much recent interest in forecasting based on factor analysis models for large numbers of observable variables (p) and large numbers of observations (T). Some nice asymptotic results have been produced showing that under certain conditions, as (p,T) ? (8, 8) principal components analysis can be used to carry out the forecasting, thereby avoiding the need to fit a full factor analysis model. However, the question of how large p needs to be in order for the asymptotic theory to provide an adequate approximation in practice is open. In this paper we develop probability bounds for the forecast accuracy of principal component forecasts for stationary processes in terms of an empirically determinable noise to signal ratio. We develop a hypothesis test for this bound for which asymptotics in T hold even with p large and apply this test to US macrodata.
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Suggested Citation

  • Victor Solo & Chris Heaton, 2003. "Asymptotic Principal Components Estimation of Large Factor Models," Computing in Economics and Finance 2003 251, Society for Computational Economics.
  • Handle: RePEc:sce:scecf3:251

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    References listed on IDEAS

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    5. Chen, Yan & Plott, Charles R., 1996. "The Groves-Ledyard mechanism: An experimental study of institutional design," Journal of Public Economics, Elsevier, vol. 59(3), pages 335-364, March.
    6. repec:cup:macdyn:v:4:y:2000:i:3:p:373-414 is not listed on IDEAS
    7. Thomas Muench & Mark Walker, 1983. "Are Groves-Ledyard Equilibria Attainable?," Review of Economic Studies, Oxford University Press, vol. 50(2), pages 393-396.
    8. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    9. Marimon, Ramon & McGrattan, Ellen & Sargent, Thomas J., 1990. "Money as a medium of exchange in an economy with artificially intelligent agents," Journal of Economic Dynamics and Control, Elsevier, vol. 14(2), pages 329-373, May.
    10. LeBaron, Blake, 2000. "Agent-based computational finance: Suggested readings and early research," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 679-702, June.
    11. repec:hoo:wpaper:e-89-28 is not listed on IDEAS
    12. Yan Chen & Fang-Fang Tang, 1998. "Learning and Incentive-Compatible Mechanisms for Public Goods Provision: An Experimental Study," Journal of Political Economy, University of Chicago Press, vol. 106(3), pages 633-662, June.
    13. Arifovic, Jasmina, 2000. "Evolutionary Algorithms In Macroeconomic Models," Macroeconomic Dynamics, Cambridge University Press, vol. 4(03), pages 373-414, September.
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    More about this item


    factor analysis; forecasting;

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods


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