scholarly journals OPTIMAL BANDWIDTH SELECTION FOR ROBUST GENERALIZED METHOD OF MOMENTS ESTIMATION

2014 ◽  
Vol 31 (5) ◽  
pp. 1054-1077 ◽  
Author(s):  
Daniel Wilhelm
Keyword(s):  
Method Of Moments ◽  
Mean Squared Error ◽  
Order Approximation ◽  
Generalized Method Of Moments ◽  
Bandwidth Selection ◽  
Estimation Procedure ◽  
Point Estimate ◽  
Optimal Bandwidth ◽  
Squared Error ◽  
Method Of Moments Estimation

A two-step generalized method of moments estimation procedure can be made robust to heteroskedasticity and autocorrelation in the data by using a nonparametric estimator of the optimal weighting matrix. This paper addresses the issue of choosing the corresponding smoothing parameter (or bandwidth) so that the resulting point estimate is optimal in a certain sense. We derive an asymptotically optimal bandwidth that minimizes a higher-order approximation to the asymptotic mean-squared error of the estimator of interest. We show that the optimal bandwidth is of the same order as the one minimizing the mean-squared error of the nonparametric plugin estimator, but the constants of proportionality are significantly different. Finally, we develop a data-driven bandwidth selection rule and show, in a simulation experiment, that it may substantially reduce the estimator’s mean-squared error relative to existing bandwidth choices, especially when the number of moment conditions is large.

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.

The impact of urbanization on energy consumption and efficiency

2017 ◽  
Vol 28 (7) ◽  
pp. 673-686 ◽  
Author(s):  
Pengfei Sheng ◽  
Yaping He ◽  
Xiaohui Guo
Keyword(s):  
Energy Consumption ◽  
Method Of Moments ◽  
Energy Use ◽  
Generalized Method Of Moments ◽  
Method Of Moments Estimation ◽  
Process Of Urbanization ◽  
Per Capita ◽  
Using Data ◽  
Impact Of Urbanization ◽  
The Impact

There is no consensus about the impact of urbanization on energy efficiency. We seek to fill this gap in literature using data from 78 countries for the period of 1995 through 2012. Extending the Stochastic Impacts by Regression on Population, Affluence, and Technology model, we identify the impact of urbanization on energy consumption and efficiency. Results of generalized method of moments estimation indicate that the process of urbanization leads to substantial increases in both the actual and the optimal energy consumption, but a decrease in efficiency of energy use. In addition, we find that the extent to which energy inefficiency correlates with urbanization is greater in countries with higher gross domestic product per capita.

scholarly journals New Bandwidth Selection for Kernel Quantile Estimators

2012 ◽  
Vol 2012 ◽  
pp. 1-18
Author(s):  
Ali Al-Kenani ◽  
Keming Yu
Keyword(s):  
Cross Validation ◽  
Kernel Smoothing ◽  
Bandwidth Selection ◽  
Unbiased Estimation ◽  
Optimal Bandwidth ◽  
Squared Error ◽  
Bandwidth Parameter ◽  
Integrated Squared Error ◽  
Selection For ◽  
Quantile Estimators

We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a mean integrated squared error curve of which the minimising value determines an optimal bandwidth. This method is shown to lead to asymptotically optimal bandwidth choice and we also provide some general theory on the performance of optimal, data-based methods of bandwidth choice. The numerical performances of the proposed methods are compared in simulations, and the new bandwidth selection is demonstrated to work very well.

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