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.