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.

EFFICIENT TWO-STEP GENERALIZED EMPIRICAL LIKELIHOOD ESTIMATION AND TESTS WITH MARTINGALE DIFFERENCES

2020 ◽  
pp. 1-40 ◽  
Author(s):  
Fei Jin ◽  
Lung-fei Lee
Keyword(s):  
Empirical Likelihood ◽  
Generalized Method Of Moments ◽  
Likelihood Estimation ◽  
Nuisance Parameters ◽  
Martingale Differences ◽  
Generalized Empirical Likelihood ◽  
Moment Functions ◽  
The Impact ◽  
Bias Terms ◽  
Parameters Of Interest

This paper considers two-step generalized empirical likelihood (GEL) estimation and tests with martingale differences when there is a computationally simple $\sqrt n-$ consistent estimator of nuisance parameters or the nuisance parameters can be eliminated with an estimating function of parameters of interest. As an initial estimate might have asymptotic impact on final estimates, we propose general C(α)-type transformed moments to eliminate the impact, and use them in the GEL framework to construct estimation and tests robust to initial estimates. This two-step approach can save computational burden as the numbers of moments and parameters are reduced. A properly constructed two-step GEL (TGEL) estimator of parameters of interest is asymptotically as efficient as the corresponding joint GEL estimator. TGEL removes several higher-order bias terms of a corresponding two-step generalized method of moments. Our moment functions at the true parameters are martingales, thus they cover some spatial and time series models. We investigate tests for parameter restrictions in the TGEL framework, which are locally as powerful as those in the joint GEL framework when the two-step estimator is efficient.

Book Reviews

2013 ◽  
Vol 51 (3) ◽  
pp. 886-888
Keyword(s):  
Inverse Problem ◽  
Empirical Likelihood ◽  
Method Of Moments ◽  
Linear Models ◽  
Choice Model ◽  
Generalized Method Of Moments ◽  
Problem Formulation ◽  
Distribution Functions ◽  
Binary Response ◽  
Quadratic Loss

Provides a conceptual and empirical understanding of basic information theoretic econometric models and methods. Discusses formulation and analysis of parametric and semiparametric linear models; method of moments, generalized method of moments, and estimating equations; a stochastic-empirical likelihood inverse problem—formulation and estimation; a stochastic empirical likelihood inverse problem—estimation and inference; Kullback–Leibler information and the maximum empirical exponential likelihood; the Cressie–Read family of divergence measures and empirical maximum likelihood functions; Cressie–Read minimum power divergence (MPD) type estimators in practice—Monte Carlo evidence of estimation and inference sampling performance; family of MPD distribution functions for the binary response-choice model; estimation and inference for the binary response model based on the MPD family of distributions; and choosing the optimal divergence under quadratic loss. Judge is a professor at the University of California, Berkeley. Mittelhammer is Regents Professor of Economic Sciences and Statistics at Washington State University.

scholarly journals Comparing Credit Risk in Islamic and Conventional Banking Using Bank-Level Panel Data

2020 ◽  
Vol 11 (2) ◽  
pp. 235-262
Author(s):  
Akhmad Akbar Susamto ◽  
Danes Quirira Octavio ◽  
Dyah Titis Kusuma Wardani
Keyword(s):  
Economic Growth ◽  
Credit Risk ◽  
Method Of Moments ◽  
Generalized Method Of Moments ◽  
Dynamic Panel ◽  
Gmm Estimator ◽  
Panel Regressions ◽  
Conventional Banks ◽  
Risk Determinants ◽  
Asset Size

Abstract: This paper investigates if there is a difference in the level of the credit risk of Islamic as compared to the level of credit risk of conventional banks. This paper further investigates the importance of various credit risk determinants and possible differences in how such determinants affect credit risk in Islamic and conventional banking industries. This paper employs dynamic panel regressions using system GMM estimators. The sample includes 11 Islamic and 95 conventional banks in Indonesia throughout 2003-2018. Based on the results, it is concluded that there is no difference in the level of the credit risk of Islamic as compared to that of conventional banks. It is also concluded that credit risk is significantly affected by current and lagged asset size, lagged financing, current profitability, lagged economic growth, and current inflation. The effect of lagged financing, current profitability, and lagged economic growth is different in Islamic and conventional banking.Abstrak: Makalah ini menganalisis apakah terdapat perbedaan antara tingkat risiko kredit pada perbankan syariah dan tingkat risiko kredit pada perbankan konvensional. Makalah ini selanjutnya juga menganalisis signifikansi faktor-faktor yang diduga mempengaruhi risiko kredit dan kemungkinan perbedaan pengaruh faktor-faktor tersebut terhadap risiko kredit pada perbankan syariah dibandingkan pada perbankan konvensional. Makalah ini menggunakan regresi panel dinamis dengan system generalized method of moments (GMM) estimator. Sampel dalam makalah ini mencakup 11 bank syariah dan 95 bank konvensional di Indonesia selama periode 2003-2018. Berdasarkan hasil analisis, dapat disimpulkan bahwa tidak terdapat perbedaan perbedaan antara tingkat risiko kredit pada perbankan syariah dan tingkat risiko kredit pada perbankan konvensional. Begitu pula, dapat disimpulkan bahwa risiko kredit secara signifikan dipengaruhi oleh ukuran aset tahun ini dan tahun lalu, pembiayaan tahun lalu, profitabilitas tahun ini, pertumbuhan ekonomi tahun lalu dan inflasi tahun ini. Pengaruh pembiayaan tahun lalu, profitabilitas tahun ini, dan pertumbuhan ekonomi tahun lalu, secara khusus berbeda pada perbankan syariah dibandingkan pada perbankan konvensional.

TWO-STEP GMM ESTIMATION OF THE ERRORS-IN-VARIABLES MODEL USING HIGH-ORDER MOMENTS

2002 ◽  
Vol 18 (3) ◽  
pp. 776-799 ◽  
Author(s):  
Timothy Erickson ◽  
Toni M. Whited
Keyword(s):  
Method Of Moments ◽  
Mean Absolute Error ◽  
Generalized Method Of Moments ◽  
Absolute Error ◽  
Covariance Matrices ◽  
High Order ◽  
Population Projections ◽  
Errors In Variables ◽  
Gmm Estimator ◽  
Seventh Order

We consider a multiple mismeasured regressor errors-in-variables model where the measurement and equation errors are independent and have moments of every order but otherwise are arbitrarily distributed. We present parsimonious two-step generalized method of moments (GMM) estimators that exploit overidentifying information contained in the high-order moments of residuals obtained by “partialling out” perfectly measured regressors. Using high-order moments requires that the GMM covariance matrices be adjusted to account for the use of estimated residuals instead of true residuals defined by population projections. This adjustment is also needed to determine the optimal GMM estimator. The estimators perform well in Monte Carlo simulations and in some cases minimize mean absolute error by using moments up to seventh order. We also determine the distributions for functions that depend on both a GMM estimate and a statistic not jointly estimated with the GMM estimate.

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