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Modeling U.S. Historical Time-Series Prices and Inflation Using Various Linear and Nonlinear Long-Memory Approaches

Author

Listed:
  • Giorgio Canarella

    (University of Nevada, Las Vegas)

  • Luis A. Gil-Alaña

    (University of Navarra)

  • Rangan Gupta

    (University of Pretoria)

  • Stephen M. Miller

    (University of Nevada, Las Vegas)

Abstract

This paper estimates the complete historical US price data by employing a relatively new statistical methodology based on long memory. We consider, in addition to the standard case, the possibility of nonlinearities in the form of nonlinear deterministic trends as well as the possibility that persistence exists at both the zero frequency and a frequencies away from zero. We model the fractional nonlinear case using Chebyshev polynomials and model the fractional cyclical structures as a Gegenbauer process. We find in the latter case that that secular (i.e., long-run) persistence and cyclical persistence matter in the behavior of prices, producing long-memory effects that imply mean reversion at both the long-run and cyclical frequencies.

Suggested Citation

  • Giorgio Canarella & Luis A. Gil-Alaña & Rangan Gupta & Stephen M. Miller, 2017. "Modeling U.S. Historical Time-Series Prices and Inflation Using Various Linear and Nonlinear Long-Memory Approaches," Working papers 2017-13, University of Connecticut, Department of Economics.
    • Handle: RePEc:uct:uconnp:2017-13
    Note: Stephen Miller is the corresponding author
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Persistence; Cyclicality; Chebyshev polynomials; Gegenbauer processes;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

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