Robustness in Statistical Forecasting

✍ Scribed by Yuriy Kharin (auth.)

• Publisher: Springer International Publishing
• Year: 2013
• Tongue: English
• Leaves: 369
• Edition: 1
• Category: Library

✦ Synopsis

Traditional procedures in the statistical forecasting of time series, which are proved to be optimal under the hypothetical model, are often not robust under relatively small distortions (misspecification, outliers, missing values, etc.), leading to actual forecast risks (mean square errors of prediction) that are much higher than the theoretical values. This monograph fills a gap in the literature on robustness in statistical forecasting, offering solutions to the following topical problems:

- developing mathematical models and descriptions of typical distortions in applied forecasting problems;

- evaluating the robustness for traditional forecasting procedures under distortions;

- obtaining the maximal distortion levels that allow the “safe” use of the traditional forecasting algorithms;

- creating new robust forecasting procedures to arrive at risks that are less sensitive to definite distortion types.

✦ Table of Contents

Front Matter....Pages i-xvi
Introduction....Pages 1-5
A Decision-Theoretic Approach to Forecasting....Pages 7-29
Time Series Models of Statistical Forecasting....Pages 31-53
Performance and Robustness Characteristics in Statistical Forecasting....Pages 55-72
Forecasting Under Regression Models of Time Series....Pages 73-104
Robustness of Time Series Forecasting Based on Regression Models....Pages 105-162
Optimality and Robustness of ARIMA Forecasting....Pages 163-230
Optimality and Robustness of Vector Autoregression Forecasting Under Missing Values....Pages 231-272
Robustness of Multivariate Time Series Forecasting Based on Systems of Simultaneous Equations....Pages 273-303
Forecasting of Discrete Time Series....Pages 305-352
Back Matter....Pages 353-356

✦ Subjects

Statistical Theory and Methods; Probability Theory and Stochastic Processes; Statistics for Business/Economics/Mathematical Finance/Insurance; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Appl.Mathe