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Model Selection in ARMA(p,q) Processes

  • John D. Levendis
Chapter
Part of the Springer Texts in Business and Economics book series (STBE)

Abstract

In practice, the form of the underlying process that generated the data is unknown. Should we estimate an AR(p) model, an MA(q) model, or an ARMA(p,q) model? Moreover, what lag lengths of p and q should we choose? We simply do not have good a priori reason to suspect that the data generating process is of one type or another, or a combination of the two. How is a researcher to proceed? Which sort of model should we estimate?

References

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  2. Box, G. E., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control (revised ed.). Oakland: Holden-Day.Google Scholar
  3. Pankratz, A. (1983). Forecasting with univariate Box-Jenkins models: concepts and cases. New York: Wiley.CrossRefGoogle Scholar
  4. Pankratz, A. (1991). Forecasting with dynamic regression models. New York: Wiley.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • John D. Levendis
    • 1
  1. 1.Department of EconomicsLoyola University New OrleansNew OrleansUSA

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