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This paper investigates inferencemethods to introduce prior information in econometric modelling throughstochastic restrictions. The goal is to show that stochastic restrictionsmethod estimator can be asymptotically more efficient than the estimatorignoring prior information and can achieve efficiency if prior informationgrows faster than the sample information in the asymptotics. The set upincludes the nonlinear least squares and indirect inference estimators. Thepaper proposes a new indirect inference estimator that incorporates stochasticequality constraints on the parameters of interest. Finally, the proposedapproach is applied to a macroeconomics model where high efficiency gains areshown.


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

Cite this paper

Hernández, J. 2016 On the Asymptotics of Stochastic Restrictions. Theoretical Economics Letters, 6, 707-725. doi: 10.4236-tel.2016.64075.

Author: José A. Hernández

Source: http://www.scirp.org/


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