Time Series Models of Statistical Forecasting

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

Chapter Chapter 3 in Robustness in Statistical Forecasting, 2013, pp 31-53 from Springer

Abstract: Abstract This chapter introduces time series models which are most commonly used in statistical forecasting: regression models, including trend models, stationary time series models, the ARIMA(p, d, q) model, nonlinear models, multivariate time series models (including VARMA(p, q) and simultaneous equations models), as well as models of discrete time series with a specific focus on high-order Markov chains.

Keywords: Time Series Model; Stationary Time Series; Discrete Time Series; Stochastic Difference Equation; Nonlinear Time Series Model (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_3

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DOI: 10.1007/978-3-319-00840-0_3

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