A Decision-Theoretic Approach to Forecasting

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

Chapter Chapter 2 in Robustness in Statistical Forecasting, 2013, pp 7-29 from Springer

Abstract: Abstract Statistical forecasting is prediction of future states of a certain process based on the available stochastic observations as well as the available prior model assumptions made about this process. This chapter describes a general (universal) approach to statistical forecasting based on mathematical decision theory, including a brief discussion of discriminant analysis. The following fundamental notions are introduced: optimal and suboptimal forecasts, loss function, risk functional, minimax, admissible, and Bayesian decision rules (BDRs), Bayesian forecast density, decision rule randomization, plug-in principle.

Keywords: Decision Rule; Conditional Probability Density; Statistical Forecast; Posterior Probability Density; Interval Forecast (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_2

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DOI: 10.1007/978-3-319-00840-0_2

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