Simulation Based Estimation Using Extended Balanced Aug- mented Empirical Likelihood
نویسندگان
چکیده
This paper introduces an extension of the balanced augmented empirical likelihood method for estimating simulation models. We analyze its performance empirically using MonteCarlo methods, and demonstrate that our new method increases the flexibility and accuracy of the empirical likelihood approach, while preserving both its limit distribution and its consistency for moment condition models. We illustrate the efficiency of our method in terms of simulation sample size by estimating the parameters of a geometric Brownian motion process.
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