scholarly journals Using Samples of Unequal Length in Generalized Method of Moments Estimation

2008 ◽  
Author(s):  
Anthony Lynch ◽  
Jessica Wachter
2013 ◽  
Vol 48 (1) ◽  
pp. 277-307 ◽  
Author(s):  
Anthony W. Lynch ◽  
Jessica A. Wachter

AbstractThis paper describes estimation methods, based on the generalized method of moments (GMM), applicable in settings where time series have different starting or ending dates. We introduce two estimators that are more efficient asymptotically than standard GMM. We apply these to estimating predictive regressions in international data and show that the use of the full sample affects inference for assets with data available over the full period as well as for assets with data available for a subset of the period. Monte Carlo experiments demonstrate that reductions hold for small-sample standard errors as well as asymptotic ones.


2017 ◽  
Vol 28 (7) ◽  
pp. 673-686 ◽  
Author(s):  
Pengfei Sheng ◽  
Yaping He ◽  
Xiaohui Guo

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.


2014 ◽  
Vol 31 (5) ◽  
pp. 1054-1077 ◽  
Author(s):  
Daniel Wilhelm

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.


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