scholarly journals Uniform Asymptotic Risk of Averaging GMM Estimator Robust to Misspecification, Second Version

2015 ◽  
Author(s):  
Xu Cheng ◽  
Zhipeng Liao ◽  
Ruoyao Shi
Keyword(s):  
Gmm Estimator ◽  
Asymptotic Risk

scholarly journals On uniform asymptotic risk of averaging GMM estimators

10.3982/qe711 ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 931-979 ◽  
Author(s):  
Xu Cheng ◽  
Zhipeng Liao ◽  
Ruoyao Shi
Keyword(s):  
Nonlinear Models ◽  
Sufficient Conditions ◽  
Risk Difference ◽  
Upper And Lower Bounds ◽  
Quadratic Loss ◽  
Finite Sample ◽  
Moment Conditions ◽  
Gmm Estimator ◽  
Asymptotic Risk ◽  
Non Gaussian

This paper studies the averaging GMM estimator that combines a conservative GMM estimator based on valid moment conditions and an aggressive GMM estimator based on both valid and possibly misspecified moment conditions, where the weight is the sample analog of an infeasible optimal weight. We establish asymptotic theory on uniform approximation of the upper and lower bounds of the finite‐sample truncated risk difference between any two estimators, which is used to compare the averaging GMM estimator and the conservative GMM estimator. Under some sufficient conditions, we show that the asymptotic lower bound of the truncated risk difference between the averaging estimator and the conservative estimator is strictly less than zero, while the asymptotic upper bound is zero uniformly over any degree of misspecification. The results apply to quadratic loss functions. This uniform asymptotic dominance is established in non‐Gaussian semiparametric nonlinear models.

scholarly journals Bank Risk Determinants in Latin America

Risks ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 94
Author(s):  
Mariña Martínez-Malvar ◽  
Laura Baselga-Pascual
Keyword(s):  
Latin America ◽  
Commercial Banks ◽  
Banking Crises ◽  
Data Driven ◽  
Bank Risk ◽  
Z Score ◽  
Analysis Method ◽  
Gmm Estimator ◽  
Main Bank ◽  
Risk Determinants

Systemic Banking crises are a recurrent phenomenon that affects society, and there is a need for a better understanding of the risk factors to support prudential regulation and reduce unnecessary risk intake in the financial system. This paper examines the main bank risk determinants in Latin America. The period analysed covers the timespan from 1999 to 2013, including the systemic banking crisis episodes in Argentina (2001–2003) and Uruguay (2002–2005). We apply a new data-driven comparable methodology to classify and select commercial banks from the sample. We study bank risk proxied by the Z-score. We use the system-GMM estimator as our main empirical analysis method. According to our results, well capitalized, liquid, and traditional commercial banks are less risky. We perform robustness tests by applying OLS, and the results resemble our original model.

A note on a cross-sectional GMM estimator in the presence of an observable common shock

Statistics ◽  
2020 ◽  
Vol 54 (2) ◽  
pp. 415-423
Author(s):  
Serguey Khovansky ◽  
Oleksandr Zhylyevskyy
Keyword(s):  
Cross Sectional ◽  
Gmm Estimator ◽  
Common Shock

scholarly journals GEL METHODS FOR NONSMOOTH MOMENT INDICATORS

2010 ◽  
Vol 27 (1) ◽  
pp. 74-113 ◽  
Author(s):  
Paulo M.D.C. Parente ◽  
Richard J. Smith
Keyword(s):  
Empirical Likelihood ◽  
Method Of Moments ◽  
Jacobian Matrix ◽  
Generalized Method Of Moments ◽  
Gmm Estimator ◽  
First Order ◽  
Generalized Empirical Likelihood ◽  
Finite Samples ◽  
Class Of Estimators ◽  
Validity Of Tests

This paper considers the first-order large sample properties of the generalized empirical likelihood (GEL) class of estimators for models specified by nonsmooth indicators. The GEL class includes a number of estimators recently introduced as alternatives to the efficient generalized method of moments (GMM) estimator that may suffer from substantial biases in finite samples. These include empirical likelihood (EL), exponential tilting (ET), and the continuous updating estimator (CUE). This paper also establishes the validity of tests suggested in the smooth moment indicators case for overidentifying restrictions and specification. In particular, a number of these tests avoid the necessity of providing an estimator for the Jacobian matrix that may be problematic for the sample sizes typically encountered in practice.

scholarly journals ON THE ASYMPTOTIC EFFICIENCY OF GMM

2013 ◽  
Vol 30 (2) ◽  
pp. 372-406 ◽  
Author(s):  
Marine Carrasco ◽  
Jean-Pierre Florens
Keyword(s):  
Reproducing Kernel ◽  
Reproducing Kernel Hilbert Space ◽  
Asymptotic Variance ◽  
Inner Product ◽  
Method Of Moment ◽  
Conditional Moment ◽  
Moment Conditions ◽  
Gmm Estimator ◽  
The Moment ◽  
Semiparametric Efficiency Bound

The efficiency of the generalized method of moment (GMM) estimator is addressed by using a characterization of its variance as an inner product in a reproducing kernel Hilbert space. We show that the GMM estimator is asymptotically as efficient as the maximum likelihood estimator if and only if the true score belongs to the closure of the linear space spanned by the moment conditions. This result generalizes former ones to autocorrelated moments and possibly infinite number of moment restrictions. Second, we derive the semiparametric efficiency bound when the observations are known to be Markov and satisfy a conditional moment restriction. We show that it coincides with the asymptotic variance of the optimal GMM estimator, thus extending results by Chamberlain (1987,Journal of Econometrics34, 305–33) to a dynamic setting. Moreover, this bound is attainable using a continuum of moment conditions.

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