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Abstract Details
Activity Number:
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38
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract - #306699 |
Title:
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Factor Model for Forecasting with Multi-Collinearity and Nonlinear Dependence
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Author(s):
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Joseph Egbulefu*+
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Companies:
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Rice University
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Address:
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2120 El Paseo Street, Houston, TX, 77054, United States
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Keywords:
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multicollinearity ;
non-linear least squares ;
dynamic factor model ;
principal component analysis ;
partial least squares ;
PCA PLS
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Abstract:
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Empirical analysis of financial time series has identified non-linear dependence properties inherent in financial variables. Factors based on Principal Component Analysis and Partial Least Squares, empirical methods used for forecasting under multi-collinearity, can be deficient in extracting certain non-linear properties. We construct a dynamic factor model for asset prices and returns using non-linear least squares to identify dependencies inherent in variables and constructing factor loadings from singular vectors of the data matrix under a suitable non-linear transformation. The method has been shown to outperform PCA and PLS forecasting when applied to high frequency exchange rates.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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