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Princeton, NJ 08544-0708
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Regression, forecast, deep learning, approximate factor model, principal components, learning indices, sliced inverse regression, dimension reduction.
sparse alternatives, thresholding, large covariance matrix estimation, Wald-test, screening, cross-sectional independence, factor pricing model
factor model; forecasting; inverse moments; learning indices; principal components; regression; sufficient dimension reduction
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