scholarly journals SPARSE TENSOR PRODUCT APPROXIMATION FOR A CLASS OF GENERALIZED METHOD OF MOMENTS ESTIMATORS

2021 ◽  
Author(s):  
Alexandros Gilch ◽  
Michael Griebel ◽  
Jens Oettershagen
Keyword(s):  
Tensor Product ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Tensor Product Approximation ◽  
Moments Estimators

scholarly journals Higher Order Mean Squared Error of Generalized Method of Moments Estimators for Nonlinear Models

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Yi Hu ◽  
Xiaohua Xia ◽  
Ying Deng ◽  
Dongmei Guo
Keyword(s):  
Method Of Moments ◽  
Mean Squared Error ◽  
Nonlinear Models ◽  
Generalized Method Of Moments ◽  
Higher Order ◽  
Mean Square ◽  
Finite Sample ◽  
Squared Error ◽  
Moments Estimators ◽  
Moment Restriction

Generalized method of moments (GMM) has been widely applied for estimation of nonlinear models in economics and finance. Although generalized method of moments has good asymptotic properties under fairly moderate regularity conditions, its finite sample performance is not very well. In order to improve the finite sample performance of generalized method of moments estimators, this paper studies higher-order mean squared error of two-step efficient generalized method of moments estimators for nonlinear models. Specially, we consider a general nonlinear regression model with endogeneity and derive the higher-order asymptotic mean square error for two-step efficient generalized method of moments estimator for this model using iterative techniques and higher-order asymptotic theories. Our theoretical results allow the number of moments to grow with sample size, and are suitable for general moment restriction models, which contains conditional moment restriction models as special cases. The higher-order mean square error can be used to compare different estimators and to construct the selection criteria for improving estimator’s finite sample performance.

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