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    Stochastic Generalized Method of Moments

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    Type
    Article
    Authors
    Yin, Guosheng
    Ma, Yanyuan
    Liang, Faming
    Yuan, Ying
    KAUST Grant Number
    KUS-C1-016-04
    Date
    2011-08-16
    Online Publication Date
    2011-08-16
    Print Publication Date
    2011-01
    Permanent link to this record
    http://hdl.handle.net/10754/624965
    
    Metadata
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    Abstract
    The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to the special structure of the GMM, we propose a new sampling-based algorithm, the stochastic GMM sampler, which replaces the multivariate minimization problem by a series of conditional sampling procedures. We develop the theoretical properties of the proposed iterative Monte Carlo method, and demonstrate its superior performance over other GMM estimation procedures in simulation studies. As an illustration, we apply the stochastic GMM sampler to a Medfly life longevity study. Supplemental materials for the article are available online. © 2011 American Statistical Association.
    Citation
    Yin G, Ma Y, Liang F, Yuan Y (2011) Stochastic Generalized Method of Moments. Journal of Computational and Graphical Statistics 20: 714–727. Available: http://dx.doi.org/10.1198/jcgs.2011.09210.
    Sponsors
    We thank the referees, associate editor, and editor for many insightful suggestions which strengthened the work immensely. Yin’s research was supported by a grant from the Research Grants Council of Hong Kong, Ma’s research was supported by a US NSF grant, Liang’s research was supported by grants from US NSF (DMS-1007457 and CMMI-0926803) and King Abdullah University of Science and Technology (KUS-C1-016-04), and Yuan’s research was supported by a U.S. National Cancer Institute R01 grant (R01CA154591-01A1).
    Publisher
    Informa UK Limited
    Journal
    Journal of Computational and Graphical Statistics
    DOI
    10.1198/jcgs.2011.09210
    ae974a485f413a2113503eed53cd6c53
    10.1198/jcgs.2011.09210
    Scopus Count
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