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Asymptotic Principal Components Estimation Of Large Factor Models

Author

Listed:
  • Chris Heaton

    (Department of Economics, Macquarie University)

  • Victor Solo

    (School of Electrical Engineering and Telecommunications, University of New South Wales)

Abstract

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.

Suggested Citation

  • Chris Heaton & Victor Solo, 2003. "Asymptotic Principal Components Estimation Of Large Factor Models," Research Papers 0303, Macquarie University, Department of Economics.
    • Handle: RePEc:mac:wpaper:0303
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    File URL: http://www.econ.mq.edu.au/research/2003/Seattle2003%20MU%20RDP.pdf
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    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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