Large dimension forecasting models and random singular value spectra
AbstractWe present a general method to detect and extract from a finite time sample statistically meaningful correlations between input and output variables of large dimensionality. Our central result is derived from the theory of free random matrices, and gives an explicit expression for the interval where singular values are expected in the absence of any true correlations between the variables under study. Our result can be seen as the natural generalization of the Mar?cenko-Pastur distribution for the case of rectangular correlation matrices. We illustrate the interest of our method on a set of macroeconomic time series.
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Bibliographic InfoPaper provided by Science & Finance, Capital Fund Management in its series Science & Finance (CFM) working paper archive with number 500066.
Date of creation: Dec 2005
Date of revision:
Other versions of this item:
- Jean-Philippe Bouchaud & Laurent Laloux & M. Augusta Miceli & Marc Potters, 2005. "Large dimension forecasting models and random singular value spectra," Papers physics/0512090, arXiv.org.
- NEP-ALL-2006-04-01 (All new papers)
- NEP-ECM-2006-04-01 (Econometrics)
- NEP-ETS-2006-04-01 (Econometric Time Series)
- NEP-FOR-2006-04-01 (Forecasting)
- NEP-MAC-2006-04-01 (Macroeconomics)
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