TY - JOUR
T1 - Stochastic Generalized Method of Moments
AU - Yin, Guosheng
AU - Ma, Yanyuan
AU - Liang, Faming
AU - Yuan, Ying
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: 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).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
PY - 2011/8/16
Y1 - 2011/8/16
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/624965
UR - http://www.tandfonline.com/doi/abs/10.1198/jcgs.2011.09210
UR - http://www.scopus.com/inward/record.url?scp=80053377942&partnerID=8YFLogxK
U2 - 10.1198/jcgs.2011.09210
DO - 10.1198/jcgs.2011.09210
M3 - Article
SN - 1061-8600
VL - 20
SP - 714
EP - 727
JO - Journal of Computational and Graphical Statistics
JF - Journal of Computational and Graphical Statistics
IS - 3
ER -