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Bayesian inference in econometric models using Monte Carlo integration. (English) Zbl 0683.62068
Summary: Methods for the systematic application of Monte Carlo integration with importance sampling to Bayesian inference in econometric models are developed. Conditions under which the numerical approximation of a posterior moment converges almost surely to the true value as the number of Monte Carlo replications increases, and the numerical accuracy of this approximation may be assessed reliably, are set forth.
Methods for the analytical verification of these conditions are discussed. Importance sampling densities are derived from multivariate normal or Student t approximations to local behavior of the posterior density at its mode. These densities are modified by automatic rescaling along each axis. The concept of relative numerical efficiency is introduced to evaluate the adequacy of a chosen importance sampling density. The practical procedures based on these innovations are illustrated in two different models.

62P20 Applications of statistics to economics
62F15 Bayesian inference
65C99 Probabilistic methods, stochastic differential equations
65D30 Numerical integration
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