Stochastic Generalized Method of Moments.

Citation data:

Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, ISSN: 1061-8600, Vol: 20, Issue: 3, Page: 714-727

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Repository URL:
PMC3286612; 3286612
Yin, Guosheng; Ma, Yanyuan; Liang, Faming; Yuan, Ying
Informa UK Limited; Taylor & Francis
Mathematics; Decision Sciences; Generalized linear model; Gibbs sampling; Iterative monte carlo; Markov chain monte carlo; Metropolis algorithm; Moment condition
article description
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.