Statistical Analysis of Time Series and Forecasting

Pergamenshchikov Serguei () and Pchelintsev Evgeny
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Pergamenshchikov Serguei: LMRS - Laboratoire de Mathématiques Raphaël Salem - UNIROUEN - Université de Rouen Normandie - NU - Normandie Université - CNRS - Centre National de la Recherche Scientifique
Pchelintsev Evgeny: SSP&QF - International Laboratory of Statistics of Stochastic Processes and Quantitative Finance - Tomsk State University [Tomsk]

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Abstract: In this course, we present the principal parts of the time series analysis. First, stationary processes and trends in times series are introduced. Then we consider the linear regression models for which we study the main problems such that point estimation, the construction of confidence intervals, hypothesis testing, and forecasting. In addition, big data models and the main methods for their analysis are presented. Finally, we introduce the autoregressive and moving average autoregressive processes (ARMA) and study their basic properties, including the problem of forecasting.

Date: 2023-02-02
New Economics Papers: this item is included in nep-des and nep-ets
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