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Forecasting Under Regression Models of Time Series

In: Robustness in Statistical Forecasting

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  • Yuriy Kharin

    (Belarusian State University, Department of Mathematical Modeling and Data Analysis)

Abstract

This chapter is devoted to statistical forecasting under regression models of time series. These models are defined as additive mixtures of regression components and random observation errors, where the regression components are determined by regression functions. In the case of complete prior information, the optimal forecasting statistic is constructed and its mean square risk is evaluated. Consistent forecasting statistics are constructed for the different levels of parametric and nonparametric uncertainty introduced in Chap. 3 , and explicit expressions are obtained for the forecast risk. A special case of regression—the logistic regression—is considered.

Suggested Citation

  • Yuriy Kharin, 2013. "Forecasting Under Regression Models of Time Series," Springer Books, in: Robustness in Statistical Forecasting, edition 127, chapter 0, pages 73-104, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-00840-0_5
    DOI: 10.1007/978-3-319-00840-0_5
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