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Indirect Inference, Nuisance Parameter, and Threshold Moving Average Models

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  • Guay, Alain
  • Scaillet, Olivier

Abstract

We analyze the modifications that occur in indirect inference when a nuisance parameter is not identified under the null hypothesis. We develop a testing procedure adapted to this simulation-based estimation method, and detail its use for detecting the threshold effect in threshold moving average models with contemporaneous and lagged asymmetries. In contrast to existing threshold models, these models allow taking into account the presence of asymmetric effects of current and lagged random shocks. We use them to measure the persistence of shocks to U.S. output.

Suggested Citation

  • Guay, Alain & Scaillet, Olivier, 2003. "Indirect Inference, Nuisance Parameter, and Threshold Moving Average Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 122-132, January.
  • Handle: RePEc:bes:jnlbes:v:21:y:2003:i:1:p:122-32
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    Cited by:

    1. Catherine Bruneau & Amine Lahiani, 2006. "Estimation d'un modèle TIMA avec asymétrie contemporaine par inférence indirecte," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 142(IV), pages 479-500, December.
    2. Grassi, Stefano & Santucci de Magistris, Paolo, 2015. "It's all about volatility of volatility: Evidence from a two-factor stochastic volatility model," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 62-78.
    3. Lahiani, A. & Scaillet, O., 2009. "Testing for threshold effect in ARFIMA models: Application to US unemployment rate data," International Journal of Forecasting, Elsevier, vol. 25(2), pages 418-428.
    4. Gonzalo, Jesus & Martinez, Oscar, 2006. "Large shocks vs. small shocks. (Or does size matter? May be so.)," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 311-347.

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