- Restrictions on Access:
- Restricted (Penn State Only).
- The sufficient forecasting (Fan, Xue and Yao, 2017) provides an effective 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 an inferential theory of sufficient forecasting within 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 without requiring the restricted linearity condition. The predictive inference of the estimated nonparametric forecasting function is obtained with nonparametrically estimated sufficient indices. We further demonstrate the power of the sufficient forecasting in an empirical study of financial markets.
- Dissertation Note:
- M.S. Pennsylvania State University 2019.
- Technical Details:
- The full text of the dissertation is available as an Adobe Acrobat .pdf file ; Adobe Acrobat Reader required to view the file.
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