scholarly journals Hypothesis Testing and Finite Sample Properties of Generalized Method of Moments Estimators: A Monte Carlo Study

1990 ◽  
Author(s):  
Ching-Sheng Mao
Keyword(s):  
Monte Carlo ◽  
Hypothesis Testing ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Monte Carlo Study ◽  
Finite Sample ◽  
Finite Sample Properties ◽  
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.

scholarly journals GMM Estimation of a Partially Linear Additive Spatial Error Model

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 622
Author(s):  
Jianbao Chen ◽  
Suli Cheng
Keyword(s):  
Monte Carlo ◽  
Method Of Moments ◽  
Arbitrary Number ◽  
Generalized Method Of Moments ◽  
Error Model ◽  
Spatial Error Model ◽  
Finite Sample ◽  
Spatial Error ◽  
Partially Linear ◽  
Consistency And Asymptotic Normality

This article presents a partially linear additive spatial error model (PLASEM) specification and its corresponding generalized method of moments (GMM). It also derives consistency and asymptotic normality of estimators for the case with a single nonparametric term and an arbitrary number of nonparametric additive terms under some regular conditions. In addition, the finite sample performance for our estimates is assessed by Monte Carlo simulations. Lastly, the proposed method is illustrated by analyzing Boston housing data.

SPATIAL DEPENDENCE IN OPTION OBSERVATION ERRORS

2020 ◽  
pp. 1-43
Author(s):  
Torben G. Andersen ◽  
Nicola Fusari ◽  
Viktor Todorov ◽  
Rasmus T. Varneskov
Keyword(s):  
Spatial Dependence ◽  
Option Price ◽  
Monte Carlo Study ◽  
Nonparametric Test ◽  
Testing Procedure ◽  
Observation Error ◽  
Finite Sample ◽  
Finite Sample Properties ◽  
Observation Times ◽  
Index Option

In this paper, we develop the first formal nonparametric test for whether the observation errors in option panels display spatial dependence. The panel consists of options with different strikes and tenors written on a given underlying asset. The asymptotic design is of the infill type—the mesh of the strike grid for the observed options shrinks asymptotically to zero, while the set of observation times and tenors for the option panel remains fixed. We propose a Portmanteau test for the null hypothesis of no spatial autocorrelation in the observation error. The test makes use of the smoothness of the true (unobserved) option price as a function of its strike and is robust to the presence of heteroskedasticity of unknown form in the observation error. A Monte Carlo study shows good finite-sample properties of the developed testing procedure and an empirical application to S&P 500 index option data reveals mild spatial dependence in the observation error, which has been declining in recent years.

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