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- W2970002954 abstract "The previous Chapter noted there are benefits of non-stationarity, so we now consider that aspect in detail. Non-stationarity can be caused by stochastic trends and shifts of data distributions. The simplest example of the first is a random walk of the kind created by Yule, where the current observation equals the previous one perturbed by a random shock. This form of integrated process occurs in economics, demography and climatology. Combinations of I(1) processes are also usually I(1), but in some situations stochastic trends can cancel to an I(0) outcome, called cointegration. Distributions can shift in many ways, but location shifts are the most pernicious forms for theory, empirical modelling, forecasting and policy. We discuss how they too can be handled, with the potential benefit of highlighting when variables are not related as assumed." @default.
- W2970002954 created "2019-09-05" @default.
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- W2970002954 date "2019-01-01" @default.
- W2970002954 modified "2023-10-16" @default.
- W2970002954 title "Making Trends and Breaks Work to Our Advantage" @default.
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- W2970002954 doi "https://doi.org/10.1007/978-3-030-21432-6_4" @default.
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