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Train, K. (2001) A Comparison of Hierarchical Bayes and Maximum Simulated Likelihood for Mixed Logit. Working Paper, Department of Economics, University of California, Berkeley.

has been cited by the following article:

• TITLE: On the Asymptotics of Stochastic Restrictions

  AUTHORS: José A. Hernández

  KEYWORDS: Prior Information, Asymptotic Approximation Distribution, Simulation Based Estimation, Nonlinear Models, Capital Stock Estimation, Variable Depreciation Rate

  JOURNAL NAME: Theoretical Economics Letters, Vol.6 No.4, August 3, 2016

  ABSTRACT: This paper investigates inference methods to introduce prior information in econometric modelling through stochastic restrictions. The goal is to show that stochastic restrictions method estimator can be asymptotically more efficient than the estimator ignoring prior information and can achieve efficiency if prior information grows faster than the sample information in the asymptotics. The set up includes the nonlinear least squares and indirect inference estimators. The paper proposes a new indirect inference estimator that incorporates stochastic equality constraints on the parameters of interest. Finally, the proposed approach is applied to a macroeconomics model where high efficiency gains are shown.