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A Comment on: “Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data”

Author

Listed:
  • Giuseppe Cavaliere
  • Thomas Mikosch
  • Anders Rahbek
  • Frederik Vilandt

Abstract

Based on the GARCH literature, Engle and Russell (1998) established consistency and asymptotic normality of the QMLE for the autoregressive conditional duration (ACD) model, assuming strict stationarity and ergodicity of the durations. Using novel arguments based on renewal process theory, we show that their results hold under the stronger requirement that durations have finite expectation. However, we demonstrate that this is not always the case under the assumption of stationary and ergodic durations. Specifically, we provide a counterexample where the MLE is asymptotically mixed normal and converges at a rate significantly slower than usual. The main difference between ACD and GARCH asymptotics is that the former must account for the number of durations in a given time span being random. As a by‐product, we present a new lemma which can be applied to analyze asymptotic properties of extremum estimators when the number of observations is random.

Suggested Citation

  • Giuseppe Cavaliere & Thomas Mikosch & Anders Rahbek & Frederik Vilandt, 2025. "A Comment on: “Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data”," Econometrica, Econometric Society, vol. 93(2), pages 719-729, March.
    • Handle: RePEc:wly:emetrp:v:93:y:2025:i:2:p:719-729
    DOI: 10.3982/ECTA21896
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    References listed on IDEAS

    as
    1. Jensen, Søren Tolver & Rahbek, Anders, 2004. "Asymptotic Inference For Nonstationary Garch," Econometric Theory, Cambridge University Press, vol. 20(6), pages 1203-1226, December.
    2. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    3. Jensen, Søren Tolver & Rahbek, Anders, 2007. "On The Law Of Large Numbers For (Geometrically) Ergodic Markov Chains," Econometric Theory, Cambridge University Press, vol. 23(4), pages 761-766, August.
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