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Optimality and Robustness of Vector Autoregression Forecasting Under Missing Values

In: Robustness in Statistical Forecasting

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  • Yuriy Kharin

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

Abstract

In this chapter, robustness of vector autoregression time series forecasting is studied under the influence of missing values—a common type of distortion which is especially characteristic of large datasets. Assuming a non-stochastic missing values template, a mean square optimal forecasting statistic is constructed in the case of prior knowledge of the VAR model parameters, and its risk instability coefficient is evaluated under missing values and model specification errors. In the case of parametric prior uncertainty, a consistent forecasting statistic and an asymptotic expansion of the corresponding forecast risk are obtained. The chapter is concluded by considering plug-in forecasting under simultaneous influence of outliers and missing values.

Suggested Citation

  • Yuriy Kharin, 2013. "Optimality and Robustness of Vector Autoregression Forecasting Under Missing Values," Springer Books, in: Robustness in Statistical Forecasting, edition 127, chapter 0, pages 231-272, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-00840-0_8
    DOI: 10.1007/978-3-319-00840-0_8
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