Asymptotic Principal Components Estimation of Large Factor Models
AbstractThere 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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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2003 with number 251.
Date of creation: 01 Aug 2003
Date of revision:
factor analysis; forecasting;
Other versions of this item:
- Chris Heaton & Victor Solo, 2003. "Asymptotic Principal Components Estimation Of Large Factor Models," Research Papers 0303, Macquarie University, Department of Economics.
- 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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