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Single step estimation of ARMA roots for nonfundamental nonstationary fractional models
[Non-fundamentalness in structural econometric models: A review]

Author

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  • Ignacio N Lobato
  • Carlos Velasco

Abstract

SummaryWe propose a single step estimator for the autoregressive and moving average roots (without imposing causality or invertibility restrictions) of a nonstationary Fractional ARMA process. These estimators employ an efficient tapering procedure, which allows for a long memory component in the process, but avoids estimating the nonstationarity component, which can be stochastic and/or deterministic. After selecting automatically the order of the model, we robustly estimate the AR and MA roots for trading volume for the thirty stocks in the Dow Jones Industrial Average Index in the last decade. Two empirical results are found. First, there is strong evidence that stock market trading volume exhibits nonfundamentalness. Second, noncausality is more common than noninvertibility.

Suggested Citation

  • Ignacio N Lobato & Carlos Velasco, 2022. "Single step estimation of ARMA roots for nonfundamental nonstationary fractional models [Non-fundamentalness in structural econometric models: A review]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 455-476.
    • Handle: RePEc:oup:emjrnl:v:25:y:2022:i:2:p:455-476.
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    File URL: http://hdl.handle.net/10.1093/ectj/utac001
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    Cited by:

    1. Alain Hecq & Daniel Velasquez-Gaviria, 2022. "Spectral estimation for mixed causal-noncausal autoregressive models," Papers 2211.13830, arXiv.org.
    2. Alain Hecq & Daniel Velasquez-Gaviria, 2023. "Spectral identification and estimation of mixed causal-noncausal invertible-noninvertible models," Papers 2310.19543, arXiv.org.

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