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Simulation Based Inference In Moving Average Models

  • Eric GHYSELS
  • Lynda KHALAF
  • Cosmé VODOUNOU

We examine several autoregressive-based estimators for the parameters of a moving average process, including the estimators initially proposed by Galbraith and Zinde-Walsh [1994] and Gouriéroux, Monfort and Renault [1993]. We also propose over-identified asymptotic-least-squares based variants of the former, and extensions of the latter based on Gallant and Tauchen's [1996] simulated method of moments. The relative performance of these estimators is assessed, with emphasis on the near-uninvertibility region. We find that, although no formal local-to-one arguments are taken into consideration, the Wald-type indirect inference method performs best at the boundary, with practically just one calibration.

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File URL: http://www.jstor.org/stable/20076364
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Article provided by ENSAE in its journal Annals of Economics and Statistics.

Volume (Year): (2003)
Issue (Month): 69 ()
Pages: 85-99

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Handle: RePEc:adr:anecst:y:2003:i:69:p:04
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  1. Gallant, A. Ronald & Tauchen, George, 1996. "Which Moments to Match?," Econometric Theory, Cambridge University Press, vol. 12(04), pages 657-681, October.
  2. Gourieroux, C & Monfort, A & Renault, E, 1993. "Indirect Inference," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages S85-118, Suppl. De.
  3. Ansley, Craig F. & Newbold, Paul, 1980. "Finite sample properties of estimators for autoregressive moving average models," Journal of Econometrics, Elsevier, vol. 13(2), pages 159-183, June.
  4. Mentz, Raul Pedro, 1977. "Estimation in the first-order moving average model through the finite autoregressive approximation : Some asymptotic results," Journal of Econometrics, Elsevier, vol. 6(2), pages 225-236, September.
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