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• W1516842702 abstract "The frequentist method of simulated minimum distance (SMD) is widely used in economics to estimate complex models with an intractable likelihood. In other disciplines, a Bayesian approach known as Approximate Bayesian Computation (ABC) is far more popular. This paper connects these two seemingly related approaches to likelihood-free estimation by means of a Reverse Sampler that uses both optimization and importance weighting to target the posterior distribution. Its hybrid features enable us to analyze an ABC estimate from the perspective of SMD. We show that an ideal ABC estimate can be obtained as a weighted average of a sequence of SMD modes, each being the minimizer of the deviations between the data and the model. This contrasts with the SMD, which is the mode of the average deviations. Using stochastic expansions, we provide a general characterization of frequentist estimators and those based on Bayesian computations including Laplace-type estimators. Their differences are illustrated using analytical examples and a simulation study of the dynamic panel model." @default.
• W1516842702 created "2016-06-24" @default.
• W1516842702 creator A5024377736 @default.
• W1516842702 creator A5056976753 @default.
• W1516842702 date "2015-01-06" @default.
• W1516842702 modified "2023-09-25" @default.
• W1516842702 title "The ABC of Simulation Estimation with Auxiliary Statistics" @default.
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• W1516842702 hasPublicationYear "2015" @default.
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