Simulation Based Inference in Moving Average Models
We examine several autoregressive-based estimators for the parameters of a moving average process, including the estimators initially proposed by Galbraith and Zinde-Walsh  and Gouriéroux, Monfort and Renault . We also propose over-identified asymptotic-least-squares based variants of the former, and extensions of the latter based on Gallant and Tauchen's  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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|Date of creation:||1995|
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- 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.
- Gourieroux, C. & Monfort, A. & Renault, E., 1992.
92.279, Toulouse - GREMAQ.
- Tauchen, George E. & Gallant, A. Ronald, 1995.
"Which Moments to Match,"
95-20, Duke University, Department of Economics.
- 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.
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