A generalized method of moments (GMM) estimator is unreliable for a large number of moment conditions, that is, it is comparable, or larger than the sample size. While classical GMM literature proposes several provisions to this problem, its Bayesian counterpart (i.e., Bayesian inference using a GMM criterion as a quasi-likelihood) almost totally ignores it. This study bridges this gap by proposing an adaptive Markov Chain Monte Carlo (MCMC) approach to a GMM inference with many moment conditions. Particularly, this study focuses on the adaptive tuning of a weighting matrix on the fly. Our proposal consists of two elements. The first is the use of the nonparametric eigenvalue-regularized precision matrix estimator, which contributes to numerical stability. The second is the random update of a weighting matrix, which substantially reduces computational cost, while maintaining the accuracy of the estimation. We then present a simulation study and real data application to compare the performance of the proposed approach with existing approaches.
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