scholarly journals Rates of Convergence of Minimum Distance Estimators and Kolmogorov's Entropy

1985 ◽  
Vol 13 (2) ◽  
pp. 768-774 ◽  
Author(s):  
Yannis G. Yatracos
Keyword(s):  
Minimum Distance ◽  
Rates Of Convergence ◽  
Minimum Distance Estimators ◽  
Kolmogorov's Entropy

POOLING ESTIMATES WITH DIFFERENT RATES OF CONVERGENCE: A MINIMUM χ2 APPROACH WITH EMPHASIS ON A SOCIAL INTERACTIONS MODEL

2009 ◽  
Vol 26 (1) ◽  
pp. 260-299 ◽  
Author(s):  
Lung-fei Lee
Keyword(s):  
Social Interactions ◽  
Distance Function ◽  
Minimum Distance ◽  
Goodness Of Fit ◽  
Rates Of Convergence ◽  
Goodness Of Fit Test ◽  
Minimum Distance Estimators ◽  
Distance Approach ◽  
Normally Distributed

This paper considers the extension of the classical minimum distance approach for the pooling of estimates with various rates of convergence. Under a setting where relatively high rates of convergence can be attained, the minimum distance estimators are shown to be consistent and asymptotically normally distributed. The constrained estimates can be efficient relative to the unconstrained ones. The minimized distance function is shown to be asymptotically χ2-distributed, and can be used as a goodness-of-fit test for the constraints. As the extension is motivated by some social interactions models, which are of interest in their own right, we discuss this approach for the estimation and testing of a social interactions model.

scholarly journals Fitting the Errors-in-variables Model Using High-order Cumulants and Moments

2017 ◽  
Vol 17 (1) ◽  
pp. 116-129 ◽  
Author(s):  
Timothy Erickson ◽  
Robert Parham ◽  
Toni M. Whited
Keyword(s):  
Method Of Moments ◽  
Minimum Distance ◽  
Generalized Method Of Moments ◽  
High Order ◽  
Moment Condition ◽  
Errors In Variables ◽  
Moment Estimation ◽  
Internal Support ◽  
Minimum Distance Estimators ◽  
High Order Cumulants

In this article, we consider a multiple mismeasured regressor errors-in-variables model. We present xtewreg, a command for using two-step generalized method of moments and minimum distance estimators that exploit overidentifying information contained in high-order cumulants or moments of the data. The command supports cumulant or moment estimation, internal support for the bootstrap with moment condition recentering, an arbitrary number of mismeasured regressors and perfectly measured regressors, and cumulants or moments up to an arbitrary degree. We also demonstrate how to use the estimators in the context of a corporate leverage regression.

Minimum distance estimators

1994 ◽  
Vol 41 (3) ◽  
pp. 291-302 ◽  
Author(s):  
T.P. Hettmansperger ◽  
I. Hueter ◽  
J. Hüsler
Keyword(s):  
Minimum Distance ◽  
Minimum Distance Estimators
Sign in / Sign up

Export Citation Format

Share Document