Abstract
This chapter deals with some advanced topics of time series analysis. We define the two concepts of stochastic trend and stochastic seasonality, introduce a few unit root and stationarity tests, as well as implement them with Python. We also elaborate on how to simulate a standard Brownian motion which is very useful in fields of finance and other disciplines. Finally, we concisely discuss Granger’s representation theorem and vector error correction models.
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Huang, C., Petukhina, A. (2022). Nonstationarity and Cointegrations. In: Applied Time Series Analysis and Forecasting with Python. Statistics and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-13584-2_9
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DOI: https://doi.org/10.1007/978-3-031-13584-2_9
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