On the Asymptotics of Stochastic Restrictions ()
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.
Share and Cite:
Hernández, J. (2016) On the Asymptotics of Stochastic Restrictions.
Theoretical Economics Letters,
6, 707-725. doi:
10.4236/tel.2016.64075.
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