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Estimating Stationary ARMA Models Efficiently

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  • Romulo A. Chumacero

    ()
    (University of Chile)

Abstract

This paper discusses the asymptotic and finite-sample properties of the Efficient Method of Moments (EMM) when applied to estimating stationary ARMA models. Issues such of identification, model selection, and testing are also discussed. The properties of these estimators are compared to those of Maximum Likelihood (ML) by means of Monte Carlo experiments for bot invertible and non-invertible ARMA models.

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Bibliographic Info

Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 1999 with number 1333.

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Date of creation: 01 Mar 1999
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Handle: RePEc:sce:scecf9:1333

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  1. Romulo Chumacero, . "Finite Sample Properties of the Efficient Method of Moments," Computing in Economics and Finance 1997 5, Society for Computational Economics.
  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. Eric GHYSELS & Lynda KHALAF & Cosmé VODOUNOU, 2003. "Simulation Based Inference In Moving Average Models," Annales d'Economie et de Statistique, ENSAE, issue 69, pages 85-99.
  4. Tauchen, George E. & Gallant, A. Ronald, 1995. "Which Moments to Match," Working Papers 95-20, Duke University, Department of Economics.
  5. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  6. Michaelides, Alexander & Ng, Serena, 2000. "Estimating the rational expectations model of speculative storage: A Monte Carlo comparison of three simulation estimators," Journal of Econometrics, Elsevier, vol. 96(2), pages 231-266, June.
  7. repec:cup:etheor:v:12:y:1996:i:4:p:657-81 is not listed on IDEAS
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