Inference for Iterated GMM Under Misspecification

Econometrica ◽  
2021 ◽  
Vol 89 (3) ◽  
pp. 1419-1447
Author(s):  
Bruce E. Hansen ◽  
Seojeong Lee
Keyword(s):  
Asymptotic Distribution ◽  
Method Of Moments ◽  
Asymptotic Variance ◽  
Generalized Method Of Moments ◽  
Gmm Estimator ◽  
Variance Matrix ◽  
Variance Estimators ◽  
Asymptotic Distribution Theory ◽  
Simulation Results ◽  
Inference Methods

This paper develops inference methods for the iterated overidentified Generalized Method of Moments (GMM) estimator. We provide conditions for the existence of the iterated estimator and an asymptotic distribution theory, which allows for mild misspecification. Moment misspecification causes bias in conventional GMM variance estimators, which can lead to severely oversized hypothesis tests. We show how to consistently estimate the correct asymptotic variance matrix. Our simulation results show that our methods are properly sized under both correct specification and mild to moderate misspecification. We illustrate the method with an application to the model of Acemoglu, Johnson, Robinson, and Yared (2008).

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 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.

scholarly journals China's Infrastructure Financing and the Role of Infrastructure in Awakening African Economies

2021 ◽  
Vol 18 (2) ◽  
pp. 1-25
Author(s):  
Michael Mitchell Omoruyi Ehizuelen
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Dynamic Models ◽  
Generalized Method Of Moments ◽  
Empirical Evaluation ◽  
Distinctive Character ◽  
Gmm Estimator ◽  
Insight Into ◽  
Agenda 2063

African economies, through Agenda 2063, recognize that developing infrastructure – transport, electricity, energy, water, and e-connectivity – will be critical for the region to assume a lasting place in the global economic system. As a result, this paper addresses the continent’s infrastructure gap and provides an important insight into the rapidly growing presence of China’s official infrastructure financing in Africa as well as the distinctive character of its involvement. In addition, the paper provides an empirical evaluation of the role of infrastructure in awakening African economies. The generalized-method-of-moments (GMM) estimator for dynamic models of panel data developed by Arellano and Bond (1991), and Arellano and Bover (1995) was employed to estimate an infrastructure-increased growth model.

scholarly journals China’s Infrastructure Financing and the Role of Infrastructure in Awakening African Economies

2021 ◽  
Vol 18 (2) ◽  
pp. 0-0
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Dynamic Models ◽  
Generalized Method Of Moments ◽  
Empirical Evaluation ◽  
Distinctive Character ◽  
Gmm Estimator ◽  
Insight Into ◽  
Agenda 2063

African economies, through Agenda 2063, recognize that developing infrastructure – transport, electricity, energy, water, and e-connectivity – will be critical for the region to assume a lasting place in the global economic system. As a result, this paper addresses the continent’s infrastructure gap and provides an important insight into the rapidly growing presence of China’s official infrastructure financing in Africa as well as the distinctive character of its involvement. In addition, the paper provides an empirical evaluation of the role of infrastructure in awakening African economies. The generalized-method-of-moments (GMM) estimator for dynamic models of panel data developed by Arellano and Bond (1991), and Arellano and Bover (1995) was employed to estimate an infrastructure-increased growth model.

scholarly journals Estimation of dynamic panel threshold model using Stata

2019 ◽  
Vol 19 (3) ◽  
pp. 685-697 ◽  
Author(s):  
Myung Hwan Seo ◽  
Sueyoul Kim ◽  
Young-Joo Kim
Keyword(s):  
Method Of Moments ◽  
Threshold Model ◽  
Asymptotic Variance ◽  
Generalized Method Of Moments ◽  
Estimation Algorithm ◽  
Dynamic Panel ◽  
Method Of Moments Estimation ◽  
Method Of Moments Estimator ◽  
Bootstrap Algorithm ◽  
Linearity Test

In this article, we develop a command, xthenreg, that implements the first-differenced generalized method of moments estimation of the dynamic panel threshold model that Seo and Shin (2016, Journal of Econometrics 195: 169–186) proposed. Furthermore, we derive the asymptotic variance formula for a kink-constrained generalized method of moments estimator of the dynamic threshold model and provide an estimation algorithm. We also propose a fast bootstrap algorithm to implement the bootstrap for the linearity test. We illustrate the use of xthenreg through a Monte Carlo simulation and an economic application.

scholarly journals Inequality and Economic Growth in Indonesia in The 2000’s

2018 ◽  
Vol 7 (2) ◽  
pp. 201-212
Author(s):  
Damayanti Simangunsong ◽  
Chen Kuang-Hui
Keyword(s):  
Economic Growth ◽  
Income Inequality ◽  
Method Of Moments ◽  
Negative Impact ◽  
Generalized Method Of Moments ◽  
Dynamic Panel ◽  
Gmm Estimator ◽  
One Step ◽  
The Government ◽  
The Right

The income inequality in Indonesia reached the highest level during the decentralization era and suspected to be the cause of the slowdown of the economic growth in the last five years to 2015. This paper investigates whether increasing inequality had a positive or negative impact on economic growth in Indonesia. Using dynamic panel and applying Generalized Method of Moments (GMM) estimator, the result concluded that there is a significant positive relationship between income inequality and economic growth. However, this study cannot draw a definite conclusion about the association for the different classes (bottom, middle, and top level) since only one-step system GMM is significant. Based on the result, it implies that the government should be more careful in regulating the inequality policy and understand more about the right mechanism of inequality and economy growth.DOI: 10.15408/sjie.v7i2.6177

An Algorithm to Estimate the Two-Way Fixed Effects Model

2016 ◽  
Vol 5 (1) ◽  
Author(s):  
Paulo Somaini ◽  
Frank A. Wolak
Keyword(s):  
Least Squares ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Asymptotic Variance ◽  
Generalized Method Of Moments ◽  
Computational Cost ◽  
Ordinary Least Squares ◽  
Fixed Effects Model ◽  
Computational Resources ◽  
Low Computational Cost

AbstractWe present an algorithm to estimate the two-way fixed effect linear model. The algorithm relies on the Frisch-Waugh-Lovell theorem and applies to ordinary least squares (OLS), two-stage least squares (TSLS) and generalized method of moments (GMM) estimators. The coefficients of interest are computed using the residuals from the projection of all variables on the two sets of fixed effects. Our algorithm has three desirable features. First, it manages memory and computational resources efficiently which speeds up the computation of the estimates. Second, it allows the researcher to estimate multiple specifications using the same set of fixed effects at a very low computational cost. Third, the asymptotic variance of the parameters of interest can be consistently estimated using standard routines on the residualized data.

Heteroskedasticity- and Autocorrelation-robust F and t Tests in Stata

2018 ◽  
Vol 18 (4) ◽  
pp. 951-980 ◽  
Author(s):  
Xiaoqing Ye ◽  
Yixiao Sun
Keyword(s):  
Method Of Moments ◽  
Instrumental Variable ◽  
Generalized Method Of Moments ◽  
Ordinary Least Squares ◽  
Type I ◽  
Variance Estimators ◽  
Normal Approximations ◽  
Chi Squared ◽  
Smoothing Parameters ◽  
Type Ii Errors

In this article, we consider time-series, ordinary least-squares, and instrumental-variable regressions and introduce a new pair of commands, har and hart, that implement more accurate heteroskedasticity- and autocorrelation-robust (HAR) F and t tests. These tests represent part of the recent progress on HAR inference. The F and t tests are based on the convenient F and t approximations and are more accurate than the conventional chi-squared and normal approximations. The underlying smoothing parameters are selected to target the type I and type II errors, which are the two fundamental objects in every hypothesis testing problem. The estimation command har and the postestimation test command hart allow for both kernel HAR variance estimators and orthonormal-series HAR variance estimators. In addition, we introduce another pair of new commands, gmmhar and gmmhart, that implement the recently developed F and t tests in a two-step generalized method of moments framework. For these commands, we opt for the orthonormal-series HAR variance estimator based on the Fourier bases because it allows us to develop convenient F and t approximations as in the first-step generalized method of moments framework. Finally, we present several examples to demonstrate these commands.

Tests for nonlinear restrictions under misspecified alternatives with an application to testing rational expectation hypotheses

2020 ◽  
Author(s):  
Anil Bera ◽  
Gabriel Montes-Rojas ◽  
Walter Sosa-Escudero ◽  
Javier Alejo
Keyword(s):  
Lagrange Multiplier ◽  
Method Of Moments ◽  
Rational Expectations ◽  
Rational Expectation ◽  
Generalized Method Of Moments ◽  
The Other ◽  
Gmm Estimator ◽  
Asymptotic Size ◽  
Lagrange Multiplier Tests

Summary This paper develops generalized method of moments-based (GMM-based) Lagrange multiplier tests for nonlinear hypotheses that are robust to locally misspecified possibly nonlinear alternatives. The procedure is based on an initial consistent GMM estimator of the parameters under a given set of nonlinear restrictions. The new test for one particular set of nonlinear hypotheses is consistent and has correct asymptotic size independently of whether the other, also nonlinear hypotheses, are correct or locally misspecified. To illustrate the usefulness of our proposed tests we consider testing rational expectations hypotheses using U.S. data.

Sign in / Sign up

Export Citation Format

Share Document