Optimality and Robustness of Vector Autoregression Forecasting Under Missing Values

Yuriy Kharin
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Yuriy Kharin: Belarusian State University, Department of Mathematical Modeling and Data Analysis

Chapter Chapter 8 in Robustness in Statistical Forecasting, 2013, pp 231-272 from Springer

Abstract: 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.

Keywords: Innovation Process; Outlier Probability; Robust Estimator; Observe Time Series; Forecast Statistic (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-00840-0_8

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DOI: 10.1007/978-3-319-00840-0_8

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