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Nonstationarity and ARIMA(p,d,q) Processes

In: Time Series Econometrics

Author

Listed:
  • John D. Levendis

    (Loyola University New Orleans)

Abstract

Many economic and financial time series do not have a constant mean. Rather they show growth or decay. The type of growth–whether deterministic or stochastic–has important implications for policy. In this chpater we examine the different ways to detrend the data. We spend particular attention on the effects of “differencing” (and over-differencing) the data, especially in the context of random walk models with and without drift. We also introduce the ARIMA class of models.

Suggested Citation

  • John D. Levendis, 2023. "Nonstationarity and ARIMA(p,d,q) Processes," Springer Texts in Business and Economics, in: Time Series Econometrics, edition 2, chapter 0, pages 105-126, Springer.
    • Handle: RePEc:spr:sptchp:978-3-031-37310-7_5
    DOI: 10.1007/978-3-031-37310-7_5
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