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Asymptotics For Gmm Estimators With Weak Instruments
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Book Synopsis Asymptotics for GMM Estimators with Weak Instruments by : James H. Stock
Download or read book Asymptotics for GMM Estimators with Weak Instruments written by James H. Stock and published by . This book was released on 1996 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops asymptotic distribution theory for generalized method of moments (GMM) estimators and test statistics when some of the parameters are well identified, but others are poorly identified because of weak instruments. The asymptotic theory entails applying empirical process theory to obtain a limiting representation of the (concentrated) objective function as a stochastic process. The general results are specialized to two leading cases, linear instrumental variables regression and GMM estimation of Euler equations obtained from the consumption-based capital asset pricing model with power utility. Numerical results of the latter model confirm that finite sample distributions can deviate substantially from normality, and indicate that these deviations are captured by the weak instrument asymptotic approximations.
Book Synopsis Asymptotic for GMM Estimators with Weak Instruments by : James H. Stock
Download or read book Asymptotic for GMM Estimators with Weak Instruments written by James H. Stock and published by . This book was released on 1996 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotics for Gmm Estimates with Weak Instruments by : James H. Stock
Download or read book Asymptotics for Gmm Estimates with Weak Instruments written by James H. Stock and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotics for GMM Estomators with Weak Instruments by : James H. Stock
Download or read book Asymptotics for GMM Estomators with Weak Instruments written by James H. Stock and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis GMM with Many Moment Conditions by : Chirok Han
Download or read book GMM with Many Moment Conditions written by Chirok Han and published by . This book was released on 2005 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Detecting Lack of Identification in GMM by : Jonathan H. Wright
Download or read book Detecting Lack of Identification in GMM written by Jonathan H. Wright and published by . This book was released on 2000 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the standard linear instrumental variables regression model, it must be assumed that the instruments are correlated with the endogenous variables in order to ensure the consistency and asymptotic normality of the usual instrumental variables estimator. Indeed, if the instruments are only slightly correlated with the endogenous variables, the conventional Gaussian asymptotic theory may still provide a very poor approximation to the finite sample distribution of the usual instrumental variables estimator. Because of the crucial role of this identification condition, it is common to test for instrument relevance by a first-stage F-test. Identification issues also arise in the generalized method of moments model, of which the linear instrumental variables model is a special case. But I know of no means, in the existing literature, of testing for identification in this model. This paper proposes a test of the null of underidentification in the generalized method of moments model.
Book Synopsis Generalized Method of Moments by : Alastair R. Hall
Download or read book Generalized Method of Moments written by Alastair R. Hall and published by OUP Oxford. This book was released on 2004-12-23 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized Method of Moments (GMM) has become one of the main statistical tools for the analysis of economic and financial data. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recent important developments in the field. Providing a comprehensive treatment of GMM estimation and inference, it is designed as a resource for both the theory and practice of GMM: it discusses and proves formally all the main statistical results, and illustrates all inference techniques using empirical examples in macroeconomics and finance. Building from the instrumental variables estimator in static linear models, it presents the asymptotic statistical theory of GMM in nonlinear dynamic models. Within this framework it covers classical results on estimation and inference techniques, such as the overidentifying restrictions test and tests of structural stability, and reviews the finite sample performance of these inference methods. And it discusses in detail recent developments on covariance matrix estimation, the impact of model misspecification, moment selection, the use of the bootstrap, and weak instrument asymptotics.
Book Synopsis Methods for Estimation and Inference in Modern Econometrics by : Stanislav Anatolyev
Download or read book Methods for Estimation and Inference in Modern Econometrics written by Stanislav Anatolyev and published by CRC Press. This book was released on 2011-06-07 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers important topics in econometrics. It discusses methods for efficient estimation in models defined by unconditional and conditional moment restrictions, inference in misspecified models, generalized empirical likelihood estimators, and alternative asymptotic approximations. The first chapter provides a general overview of established nonparametric and parametric approaches to estimation and conventional frameworks for statistical inference. The next several chapters focus on the estimation of models based on moment restrictions implied by economic theory. The final chapters cover nonconventional asymptotic tools that lead to improved finite-sample inference.
Book Synopsis A Practitioner's Guide to Robust Covariance Matrix Estimation by : Wouter J. Den Haan
Download or read book A Practitioner's Guide to Robust Covariance Matrix Estimation written by Wouter J. Den Haan and published by . This book was released on 1996 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper develops asymptotic distribution theory for generalized method of moments (GMM) estimators and test statistics when some of the parameters are well identified, but others are poorly identified because of weak instruments. The asymptotic theory entails applying empirical process theory to obtain a limiting representation of the (concentrated) objective function as a stochastic process. The general results are specialized to two leading cases, linear instrumental variables regression and GMM estimation of Euler equations obtained from the consumption-based capital asset pricing model with power utility. Numerical results of the latter model confirm that finite sample distributions can deviate substantially from normality, and indicate that these deviations are captured by the weak instruments asymptotic approximations.
Book Synopsis Generalized Method of Moments by : Alastair R. Hall
Download or read book Generalized Method of Moments written by Alastair R. Hall and published by Oxford University Press. This book was released on 2005 with total page 413 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized Method of Moments (GMM) has become one of the main statistical tools for the analysis of economic and financial data. This book is the first to provide an intuitive introduction to the method combined with a unified treatment of GMM statistical theory and a survey of recentimportant developments in the field. Providing a comprehensive treatment of GMM estimation and inference, it is designed as a resource for both the theory and practice of GMM: it discusses and proves formally all the main statistical results, and illustrates all inference techniques using empiricalexamples in macroeconomics and finance.Building from the instrumental variables estimator in static linear models, it presents the asymptotic statistical theory of GMM in nonlinear dynamic models. Within this framework it covers classical results on estimation and inference techniques, such as the overidentifying restrictions test andtests of structural stability, and reviews the finite sample performance of these inference methods. And it discusses in detail recent developments on covariance matrix estimation, the impact of model misspecification, moment selection, the use of the bootstrap, and weak instrumentasymptotics.
Book Synopsis A Subset-Continuous-Updating Transformation on GMM Estimators for Dynamic Panel Data Models by : Richard A. Ashley
Download or read book A Subset-Continuous-Updating Transformation on GMM Estimators for Dynamic Panel Data Models written by Richard A. Ashley and published by . This book was released on 2016 with total page 15 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-step GMM estimators of Arellano and Bond (1991) and Blundell and Bond (1998) for dynamic panel data models have been widely used in empirical work; however, neither of them performs well in small samples with weak instruments. The continuous-updating GMM estimator proposed by Hansen, Heaton and Yaron (1996) is in principle able to reduce the small-sample bias but it involves high-dimensional optimizations when the number of regressors is large. This paper proposes a computationally feasible variation on the standard two-step GMM estimators by applying the idea of continuous-updating on the autoregressive parameter only, given the fact that the absolute value of the autoregressive parameter is less than unity for a dynamic panel data model to be stationary. We show that our subset-continuous-updating transformation does not alter the asymptotic distribution of the two-step GMM estimators and it therefore retains consistency. Our simulation results indicate that the transformed GMM estimators significantly outperform their standard two-step counterparts in small samples.
Book Synopsis A Pretest and Higher Order Expansions in GMM for Nearly Weak Instruments by : Mehmet Caner
Download or read book A Pretest and Higher Order Expansions in GMM for Nearly Weak Instruments written by Mehmet Caner and published by . This book was released on 2006 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper we analyze GMM with strong and nearly-weak instruments. In the nearly weak-GMM, the correlation between the instruments and the first order conditions decline at a slower rate than root T. We find an important difference between the nearly-weak case and the weak case. Inference with point estimates is possible with Wald, Likelihood Ratio and Lagrange Multiplier tests in GMM with semi-weak instruments. The limit is the standard X squared limit. This is important from an applied perspective since tests on the weak case do depend on the true value and can only test simple null. Even though we may have all nearly-weak instruments in GMM it is still possible to test various hypothesis of interest. We also find a difference between nearly-weak and standard GMM cases. We derive higher order expansions for test statistics in the nearly-weak case, and we show that with declining quality of instruments finite sample behavior of these tests get worse, so standard GMM finite sample behavior is always better than nearly-weak GMM. Unlike the standard GMM, in the nearly-weak GMM we can not eliminate the second order terms from these test statistic's expansions. Wald test in Continuous Updating Estimator (CUE) have desirable properties in higher order expansions in the nearly-weak case. We also propose a pretest, bootstrap Kolmogorov-Smirnov test, to differentiate between weak and nearly-weak asymptotics. This is based on bootstrapping Wald CUE test. Since Wald CUE test has different limits under weak and nearly-weak cases and has good properties in the higher order expansions its empirical distribution can be used in a pretest. We also conduct some simulations and show that some of the asset pricing models conform to nearly-weak asymptotics. Clearly, this paper shows that when we move away from the standard GMM towards nearly-weak case, finite sample behavior suffers but large sample theory remains intact. But if we further move away from the nearly-weak case to weakly identified GMM the large sample theory changes too.
Book Synopsis Generalized Method of Moments Estimation by : Laszlo Matyas
Download or read book Generalized Method of Moments Estimation written by Laszlo Matyas and published by Cambridge University Press. This book was released on 1999-04-13 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: The generalized method of moments (GMM) estimation has emerged as providing a ready to use, flexible tool of application to a large number of econometric and economic models by relying on mild, plausible assumptions. The principal objective of this volume is to offer a complete presentation of the theory of GMM estimation as well as insights into the use of these methods in empirical studies. It is also designed to serve as a unified framework for teaching estimation theory in econometrics. Contributors to the volume include well-known authorities in the field based in North America, the UK/Europe, and Australia. The work is likely to become a standard reference for graduate students and professionals in economics, statistics, financial modeling, and applied mathematics.
Book Synopsis Conditional Moment Estimation of Nonlinear Equation Systems by : Joachim Inkmann
Download or read book Conditional Moment Estimation of Nonlinear Equation Systems written by Joachim Inkmann and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generalized method of moments (GMM) estimation of nonlinear systems has two important advantages over conventional maximum likelihood (ML) estimation: GMM estimation usually requires less restrictive distributional assumptions and remains computationally attractive when ML estimation becomes burdensome or even impossible. This book presents an in-depth treatment of the conditional moment approach to GMM estimation of models frequently encountered in applied microeconometrics. It covers both large sample and small sample properties of conditional moment estimators and provides an application to empirical industrial organization. With its comprehensive and up-to-date coverage of the subject which includes topics like bootstrapping and empirical likelihood techniques, the book addresses scientists, graduate students and professionals in applied econometrics.
Book Synopsis Identification and Inference for Econometric Models by : Donald W. K. Andrews
Download or read book Identification and Inference for Econometric Models written by Donald W. K. Andrews and published by Cambridge University Press. This book was released on 2005-07-04 with total page 589 pages. Available in PDF, EPUB and Kindle. Book excerpt: This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.
Book Synopsis GMM Estimation and Uniform Subvector Inference with Possible Identification Failure by : Donald W. K. Andrews
Download or read book GMM Estimation and Uniform Subvector Inference with Possible Identification Failure written by Donald W. K. Andrews and published by . This book was released on 2013 with total page 86 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper determines the properties of standard generalized method of moments (GMM) estimators, tests, and confidence sets (CS's) in moment condition models in which some parameters are unidentified or weakly identified in part of the parameter space. The asymptotic distributions of GMM estimators are established under a full range of drifting sequences of true parameters and distributions. The asymptotic sizes (in a uniform sense) of standard GMM tests and CS's are established.The paper also establishes the correct asymptotic sizes of "robust" GMM-based Wald, t; and quasi-likelihood ratio tests and CS's whose critical values are designed to yield robustness to identification problems.The results of the paper are applied to a nonlinear regression model with endogeneity and a probit model with endogeneity and possibly weak instrumental variables.
Book Synopsis GMM Estimation of Dynamic Panel Data Models with Persistent Data by : Hugo Kruiniger
Download or read book GMM Estimation of Dynamic Panel Data Models with Persistent Data written by Hugo Kruiniger and published by . This book was released on 2002 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers GMM based estimation and testing procedures for two versions of the AR(1) model with Fixed Effects, henceforth abbreviated as ARFE(1): the conditional ARFE(1) model, and the inclusive ARFE(1) model, which contains the stationary ARFE(1) models and the ARFE(1) model with a unit root. First, the paper presents a two-step Optimal Linear GMM (OLGMM) estimator for the inclusive model, which is asymptotically equivalent to the optimal nonlinear GMM estimator of Ahn and Schmidt (1997). Then the paper examines the properties of the GMM estimators for both versions of the model when the data are persistent. Among other things, we find that the OLGMM estimator is superefficient in the unit root case. Furthermore, under stationarity the covariances of the instruments of the Arellano-Bond estimator and the first differences of the dependent variable are not weak. We also derive new approximations to the finite sample distributions of the Arellano-Bond estimator (for both versions of the model), the Arellano-Bover estimator, and the System estimator. We employ local-to-zero asymptotics (cf Staiger and Stock (1997)) for the Arellano-Bond estimator for the conditional model, because its instruments are weak in this context, and we employ local-to-unity asymptotics, which is developed in this paper, for the estimators for the stationary model. The new approximations agree well with the Monte Carlo evidence in terms of bias and variance. Finally, various GMM based unit root tests against stationary and conditional alternatives are proposed.
