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Univariate Stationary Processes

In: Introduction to Modern Time Series Analysis

Author

Listed:
  • Gebhard Kirchgässner

    (University of St. Gallen)

  • Jürgen Wolters

    (Freie Universität Berlin)

Abstract

As mentioned in the introduction, the publication of the textbook by George E.P. Box and Gwilym M. Jenkins in 1970 opened a new road to the analysis of economic time series. This chapter presents the Box-Jenkins Approach, its different models and their basic properties in a rather elementary and heuristic way. These models have become an indispensable tool for short-run forecasts. We first present the most important approaches for statistical modelling of time series. These are autoregressive (AR) processes (Section 2.1) and moving average (MA) processes (Section 2.2), as well as a combination of both types, the so-called ARMA processes (Section 2.3). In Section 2.4 we show how this class of models can be used for predicting the future development of a time series in an optimal way. Finally, we conclude this chapter with some remarks on the relation between the univariate time series models described in this chapter and the simultaneous equations systems of traditional econometrics (Section 2.5).

Suggested Citation

  • Gebhard Kirchgässner & Jürgen Wolters, 2007. "Univariate Stationary Processes," Springer Books, in: Introduction to Modern Time Series Analysis, chapter 2, pages 27-91, Springer.
    • Handle: RePEc:spr:sprchp:978-3-540-73291-4_2
    DOI: 10.1007/978-3-540-73291-4_2
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    Cited by:

    1. Moahmed Hassan, Hisham & Haleeb, Amin, 2020. "Modelling GDP for Sudan using ARIMA," MPRA Paper 101207, University Library of Munich, Germany.
    2. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.

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