Simulation Based Inference in Moving Average Models
AbstractWe examine several autoregressive-based estimators for the parameters of a moving average process, including the estimators initially proposed by Galbraith and Zinde-Walsh  and GouriÃ©roux, 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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Bibliographic InfoPaper provided by Centre interuniversitaire de recherche en économie quantitative, CIREQ in its series Cahiers de recherche with number 9513.
Length: 7 pages
Date of creation: 1995
Date of revision:
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Other versions of this item:
- 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.
- Ghysels, E. & Khalaf, L. & Vodounou, C., 1995. "Simulation Based Inference in Moving Average Models," Cahiers de recherche 9513, Universite de Montreal, Departement de sciences economiques.
- Eric Ghysels & Lynda Khalaf & Cosme Vodounou, 1994. "Simulation Based Inference in Moving Average Models," CIRANO Working Papers 94s-11, CIRANO.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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