Bayesian generalized method of moments

We propose the Bayesian generalized method of moments (GMM), which is particularly useful when likelihood-based methods are di-cult. By de- riving the moments and concatenating them together, we build up a weighted quadratic objective function in the GMM framework. As in a normal density function, we take the negative GMM quadratic function divided by two and ex- ponentiate it to substitute for the usual likelihood. After specifying the prior dis- tributions, we apply the Markov chain Monte Carlo procedure to sample from the posterior distribution. We carry out simulation studies to examine the proposed Bayesian GMM procedure, and illustrate it with a real data example.

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