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Modelling Trends and Cycles in Economic Time SeriesStochastic Trends and Cycles

Modelling Trends and Cycles in Economic Time Series: Stochastic Trends and Cycles [This chapter begins by introducing the concept of stochastic trends through the use of autoregressive-integrated-moving average (ARIMA) models. It is shown that the order of integration, or degree of differencing, that a time series exhibits is of fundamental importance for determining the properties of stochastic trends and methods for selecting the order of integration are developed through detailed examples of ARIMA modelling. The crucial distinction between trend and difference stationarity is then introduced, and methods to distinguish between the two using unit root tests and to estimate trends robustly are discussed. Breaking and segmented trends in the possible presence of unit roots are then analysed. Unobserved component models are investigated, including the Beveridge-Nelson decomposition, and the signal extraction approach via the Kalman filter is used to estimate such ‘structural’ models. Further reading and background material is provided.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Modelling Trends and Cycles in Economic Time SeriesStochastic Trends and Cycles

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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021. 1st edition: © Terence C. Mills 2003
ISBN
978-3-030-76358-9
Pages
43 –90
DOI
10.1007/978-3-030-76359-6_3
Publisher site
See Chapter on Publisher Site

Abstract

[This chapter begins by introducing the concept of stochastic trends through the use of autoregressive-integrated-moving average (ARIMA) models. It is shown that the order of integration, or degree of differencing, that a time series exhibits is of fundamental importance for determining the properties of stochastic trends and methods for selecting the order of integration are developed through detailed examples of ARIMA modelling. The crucial distinction between trend and difference stationarity is then introduced, and methods to distinguish between the two using unit root tests and to estimate trends robustly are discussed. Breaking and segmented trends in the possible presence of unit roots are then analysed. Unobserved component models are investigated, including the Beveridge-Nelson decomposition, and the signal extraction approach via the Kalman filter is used to estimate such ‘structural’ models. Further reading and background material is provided.]

Published: Jul 30, 2021

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