Abstract
Most time-series methods are only valid if the underlying time-series is stationary. The more stationary something is, the more predictable it is. More specifically, a time-series is stationary if its mean, variance, and autocovariance do not rely on the particular time period.
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Notes
- 1.
Stationarity of mean, variance, and covariance is called “weak stationarity.” If all moments, including higher order moments like skewness and kurtosis, area also constant, then we say the time series has “strong form stationarity,” “strict stationarity” or has “strong stationarity.” For the purposes of this book, “stationarity” will refer to “weak stationarity.”
- 2.
In this chapter, we will be exploring primarily stationarity in the means of processes. This is often called “stability” and is a subset of stationarity. Since we do not explore non-stationary variance until Chap. 9, though, we will treat “stability” and “stationarity” as synonyms and use them interchangeably.
References
Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of Econometrics, 2(2), 111–120.
Stralkowski, C., & Wu, S. (1968). Charts for the interpretation of low order autoregressive moving average models (Technical report 164), University of Wisconsin, Department of Statistics.
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Levendis, J.D. (2018). Stationarity and Invertibility. In: Time Series Econometrics. Springer Texts in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-98282-3_4
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DOI: https://doi.org/10.1007/978-3-319-98282-3_4
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