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Activity Number: 64 - BandE Student Paper Awards
Type: Topic Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Business and Economic Statistics Section
Abstract #328645 Presentation
Title: Nonparametric Estimation of Sufficient Forecasting Using High-Dimensional Predictors
Author(s): Xiufan Yu* and Jiawei Yao and Lingzhou Xue
Companies: Penn State University and Citadel LLC and Penn State University and National Institute of Statistical Sciences
Keywords: factor model; forecasting; principal components; nonparametric estimation

The sufficient forecasting (Fan et al., 2017) provides an effective nonparametric forecasting procedure to estimate sufficient indices from high-dimensional predictors in the presence of a possible nonlinear forecast function. In this paper, we first revisit the sufficient forecasting, and explore its underlying connections to Fama-Macbeth regression and partial least squares. Then, we develop a unified nonparametric estimation procedure for sufficient forecasting under the high-dimensional framework with large cross sections, a large time dimension and a diverging number of factors. We derive the rate of convergence of the estimated factors and loadings, and characterize the asymptotic behavior of the estimated sufficient forecasting directions. We obtain the predictive inference for the estimated nonparametric forecast function with nonparametrically estimated sufficient indices. We further demonstrate the power of the sufficient forecasting in an empirical study of financial markets.

Authors who are presenting talks have a * after their name.

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