Download or read book A Transformed System GMM Estimator for Dynamic Panel Data Models written by Xiaojin Sun and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The system GMM estimator developed by Blundell and Bond (1998) for dynamic panel data models has been widely used in empirical work; however, it does not perform well with weak instruments. This paper proposes a variation on the system GMM estimator, based on a simple transformation of the dependent variable. Simulation results indicate that, infinite samples, this transformed system GMM estimator greatly outperforms its conventional counterpart in estimating the coefficient of the lagged dependent variable, especially when the variation in the fixed effects is large relative to that in the idiosyncratic shocks and when the dependent variable is highly persistent. Applying this transformation also substantially strengthens the reliability of inferences on the overall model specification based upon the Sargan/Hansen test. As illustrations, the transformed system GMM estimator is applied to two empirical examples from the literature: a production function and an employment equation.

Download or read book Further Results on The Weak Instruments Problem of the System GMM Estimator in Dynamic Panel Data Models written by Kazuhiko Hayakawa and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we investigate the weak instruments problem of the generalized method of moments (GMM) estimator for dynamic panel data models. Specifically, we complement Bun and Windmeijer (2010) by considering the alternative first-difference and level models transformed by the forward GLS transformation. We demonstrate that this transformation yields a higher concentration parameter compared with the original models. This indicates that the proposed transformation yields stronger instruments even though the instruments used are identical. The Monte Carlo simulation results show that the system GMM estimator for the transformed model, called the forward system GMM estimator, performs better than the conventional system GMM estimator.

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.

Download or read book Panel Data Econometrics written by Manuel Arellano and published by Oxford University Press. This book was released on 2003 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by one of the world's leading experts on dynamic panel data reviews, this volume reviews most of the important topics in the subject. It deals with static models, dynamic models, discrete choice and related models.

Download or read book The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models written by and published by . This book was released on 2007 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Download or read book The Weak Instrument Problem of the System GMM Estimator in Dynamic Panel Data Models written by Maurice Josephus Gerardus Bun and published by . This book was released on 2009 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficiency Comparisons for a System GMM Estimator in Dynamic Panel Data Models written by Frank A. G. Windmeijer and published by . This book was released on 1998 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book GMM Estimation of Fixed Effects Dynamic Panel Data Models with Spatial Lag and Spatial Errors written by Pavel Čížek and published by . This book was released on 2015 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Asymptotic Properties of the System GMM Estimator in Dynamic Panel Data Models When Both N and T are Large written by Kazuhiko Hayakawa and published by . This book was released on 2014 with total page 59 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we derive the asymptotic properties of the system GMM estimator in dynamic panel data models with individual and time effects when both N and T, the dimensions of cross section and time series, are large. We first show that the two-step level GMM estimator with an optimal weighting matrix is consistent under large N and T asymptotics, whereas that with a non-optimal one is not. We then show that the two-step system GMM estimator is consistent even if a sub-optimal weighting matrix where off-diagonal blocks are set to zero is used. Such consistency results theoretically support the use of the system GMM estimator in large N and T contexts even though it was originally developed for large N and small T panels. Simulation results indicate that the large N and large T asymptotic results approximate the finite sample behavior reasonably well unless persistency of data is strong and/or the variance ratio of individual effects to the disturbances is large.

Download or read book The Oxford Handbook of Panel Data written by Badi Hani Baltagi and published by . This book was released on 2015 with total page 705 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Oxford Handbook of Panel Data examines new developments in the theory and applications of panel data. It includes basic topics like non-stationary panels, co-integration in panels, multifactor panel models, panel unit roots, measurement error in panels, incidental parameters and dynamic panels, spatial panels, nonparametric panel data, random coefficients, treatment effects, sample selection, count panel data, limited dependent variable panel models, unbalanced panel models with interactive effects and influential observations in panel data. Contributors to the Handbook explore applications of panel data to a wide range of topics in economics, including health, labor, marketing, trade, productivity, and macro applications in panels. This Handbook is an informative and comprehensive guide for both those who are relatively new to the field and for those wishing to extend their knowledge to the frontier. It is a trusted and definitive source on panel data, having been edited by Professor Badi Baltagi-widely recognized as one of the foremost econometricians in the area of panel data econometrics. Professor Baltagi has successfully recruited an all-star cast of experts for each of the well-chosen topics in the Handbook.

Download or read book Regression Analysis of Count Data written by Adrian Colin Cameron and published by Cambridge University Press. This book was released on 2013-05-27 with total page 597 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.

Download or read book Panel Data Econometrics with R written by Yves Croissant and published by John Wiley & Sons. This book was released on 2018-08-10 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.

Download or read book On the Behavior of the GMM Estimator in Persistent Dynamic Panel Data Models with Unrestricted Initial Conditions written by Kazuhiko Hayakawa and published by . This book was released on 2016 with total page 41 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper investigates the behavior of the first-difference(FD) GMM estimator for dynamic panel data models when the persistency of data is (moderately) strong and the initial conditions are unrestricted. We show that both the initial conditions and the degree of persistency affect the rate of convergence of the GMM estimator under a local to unity system where the autoregressive parameter is modeled as $ alpha_N=1-c/N^p$, where $N$ is the cross-sectional sample size and $0

Download or read book Estimating Panel Data Models with Endogeneity and Selection written by Anastasia Semykina and published by . This book was released on 2006 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Maximum Likelihood and GMM Estimation of Dynamic Panel Data Models with Fixed Effects 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 inference procedures for two types of dynamic linear panel data models with fixed effects (FE). First, it shows that the closures of stationary ARMAFE models can be consistently estimated by Conditional Maximum Likelihood Estimators and it derives their asymptotic distributions. Then it presents an asymptotically equivalent Minimum Distance Estimator which permits an analytic comparison between the CMLE for the ARFE (1) model and the GMM estimators that have been considered in the literature. The CMLE is shown to be asymptotically less efficient than the most efficient GMM estimator when N approaches the limit infinity but T is fixed. Under normality some of the moment conditions become asymptotically redundant and the CMLE attains the Cramer-Rao lowerbound when T approaches the limit infinity as well. The paper also presents likelihood based unit root tests. Finally, the properties of CML, GMM, and Modified ML estimators for dynamic panel data models that condition on the initial observations are studied and compared. It is shown that for finite T the MMLE is less efficient than the most efficient GMM estimator.

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.