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 Conceptual Econometrics Using R written by and published by Elsevier. This book was released on 2019-08-20 with total page 332 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conceptual Econometrics Using R, Volume 41 provides state-of-the-art information on important topics in econometrics, including quantitative game theory, multivariate GARCH, stochastic frontiers, fractional responses, specification testing and model selection, exogeneity testing, causal analysis and forecasting, GMM models, asset bubbles and crises, corporate investments, classification, forecasting, nonstandard problems, cointegration, productivity and financial market jumps and co-jumps, among others. - Presents chapters authored by distinguished, honored researchers who have received awards from the Journal of Econometrics or the Econometric Society - Includes descriptions and links to resources and free open source R, allowing readers to not only use the tools on their own data, but also jumpstart their understanding of the state-of-the-art

Download or read book The Econometrics of Multi dimensional Panels written by Laszlo Matyas and published by Springer. This book was released on 2017-07-26 with total page 467 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the econometric foundations and applications of multi-dimensional panels, including modern methods of big data analysis. The last two decades or so, the use of panel data has become a standard in many areas of economic analysis. The available models formulations became more complex, the estimation and hypothesis testing methods more sophisticated. The interaction between economics and econometrics resulted in a huge publication output, deepening and widening immensely our knowledge and understanding in both. The traditional panel data, by nature, are two-dimensional. Lately, however, as part of the big data revolution, there has been a rapid emergence of three, four and even higher dimensional panel data sets. These have started to be used to study the flow of goods, capital, and services, but also some other economic phenomena that can be better understood in higher dimensions. Oddly, applications rushed ahead of theory in this field. This book is aimed at filling this widening gap. The first theoretical part of the volume is providing the econometric foundations to deal with these new high-dimensional panel data sets. It not only synthesizes our current knowledge, but mostly, presents new research results. The second empirical part of the book provides insight into the most relevant applications in this area. These chapters are a mixture of surveys and new results, always focusing on the econometric problems and feasible solutions.

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 Handbook of Research Methods and Applications in Empirical Microeconomics written by Hashimzade, Nigar and published by Edward Elgar Publishing. This book was released on 2021-11-18 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written in a comprehensive yet accessible style, this Handbook introduces readers to a range of modern empirical methods with applications in microeconomics, illustrating how to use two of the most popular software packages, Stata and R, in microeconometric applications.

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 Handbook of Research Methods and Applications in Empirical Macroeconomics written by Nigar Hashimzade and published by Edward Elgar Publishing. This book was released on 2013-01-01 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive Handbook presents the current state of art in the theory and methodology of macroeconomic data analysis. It is intended as a reference for graduate students and researchers interested in exploring new methodologies, but can also be employed as a graduate text. The Handbook concentrates on the most important issues, models and techniques for research in macroeconomics, and highlights the core methodologies and their empirical application in an accessible manner. Each chapter is largely self-contained, whilst the comprehensive introduction provides an overview of the key statistical concepts and methods. All of the chapters include the essential references for each topic and provide a sound guide for further reading. Topics covered include unit roots, non-linearities and structural breaks, time aggregation, forecasting, the Kalman filter, generalised method of moments, maximum likelihood and Bayesian estimation, vector autoregressive, dynamic stochastic general equilibrium and dynamic panel models. Presenting the most important models and techniques for empirical research, this Handbook will appeal to students, researchers and academics working in empirical macro and econometrics.

Download or read book Large N and Large T Properties of Panel Data Estimators and The Hausman Test written by Seung C. Ahn and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper examines the asymptotic properties of the popular within, GLS estimators and the Hausman test for panel data models with both large numbers of cross-section (N) and time-series (T) observations. The model we consider includes the regressors with deterministic trends in mean as well as time invariant regressors. If a time-varying regressor is correlated with time invariant regressors, the time series of the time-varying regressor is not ergodic. Our asymptotic results are obtained considering the dependence of such non-ergodic time-varying regressors. We find that the within estimator is as efficient as the GLS estimator. Despite this asymptotic equivalence, however, the Hausman statistic, which is essentially a distance measure between the two estimators, is well defined and asymptotically chi square-distributed under the random effects assumption.

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 Improved GMM Estimation of Panel VAR Models written by Kazuhiko Hayakawa and published by . This book was released on 2015 with total page 30 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, improved IV/GMM estimators for panel vector autoregressive models (VAR) are proposed by extending Hayakawa (2009b) ("A Simple Efficient Instrumental Variable Estimator in Panel AR(p) Models When Both N and T Are Large,'' Econometric Theory, 25, 873-890) in which an alternative form of instruments is suggested. It is shown that the proposed IV estimator has the same asymptotic distribution as the bias-corrected fixed effects estimator of Hahn and Kuersteiner (2002) ("Asymptotically Unbiased Inference for a Dynamic Panel Model with Fixed Effects When Both n and T Are Large,'' Econometrica, 70, 1639-1657) in the VAR(1) case when both N and T are large where N and T denote the sample sizes of cross section and time series, respectively. Since the proposed estimator is simply to change the form of instruments, it is very easy to implement in practice. As applications of the proposed estimators, we consider a panel Granger causality test and panel impulse response analysis in which the asymptotic distribution of generalized impulse response functions of Pesaran and Shin (1998) ("Generalized Impulse Response Analysis in Linear Multivariate Models,'' Economics Letters, 58, 17-29) is newly derived. Monte Carlo simulation results show that the proposed estimators have comparable or better finite sample properties than the conventional IV/GMM estimators using instruments in levels for moderate or large T.

Download or read book Econometric Analysis of Panel Data written by Badi H. Baltagi and published by Springer Nature. This book was released on 2021-03-16 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook offers a comprehensive introduction to panel data econometrics, an area that has enjoyed considerable growth over the last two decades. Micro and Macro panels are becoming increasingly available, and methods for dealing with these types of data are in high demand among practitioners. Software programs have fostered this growth, including freely available programs in R and numerous user-written programs in both Stata and EViews. Written by one of the world’s leading researchers and authors in the field, Econometric Analysis of Panel Data has established itself as the leading textbook for graduate and postgraduate courses on panel data. It provides up-to-date coverage of basic panel data techniques, illustrated with real economic applications and datasets, which are available at the book’s website on springer.com. This new sixth edition has been fully revised and updated, and includes new material on dynamic panels, limited dependent variables and nonstationary panels, as well as spatial panel data. The author also provides empirical illustrations and examples using Stata and EViews. “This is a definitive book written by one of the architects of modern, panel data econometrics. It provides both a practical introduction to the subject matter, as well as a thorough discussion of the underlying statistical principles without taxing the reader too greatly." Professor Kajal Lahiri, State University of New York, Albany, USA. "This book is the most comprehensive work available on panel data. It is written by one of the leading contributors to the field, and is notable for its encyclopaedic coverage and its clarity of exposition. It is useful to theorists and to people doing applied work using panel data. It is valuable as a text for a course in panel data, as a supplementary text for more general courses in econometrics, and as a reference." Professor Peter Schmidt, Michigan State University, USA. “Panel data econometrics is in its ascendancy, combining the power of cross section averaging with all the subtleties of temporal and spatial dependence. Badi Baltagi provides a remarkable roadmap of this fascinating interface of econometric method, enticing the novitiate with technical gentleness, the expert with comprehensive coverage and the practitioner with many empirical applications.” Professor Peter C. B. Phillips, Cowles Foundation, Yale University, USA.

Download or read book Analysis of Panel Data written by Cheng Hsiao and published by Cambridge University Press. This book was released on 2014-12-08 with total page 563 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive, coherent, and intuitive review of panel data methodologies that are useful for empirical analysis. Substantially revised from the second edition, it includes two new chapters on modeling cross-sectionally dependent data and dynamic systems of equations. Some of the more complicated concepts have been further streamlined. Other new material includes correlated random coefficient models, pseudo-panels, duration and count data models, quantile analysis, and alternative approaches for controlling the impact of unobserved heterogeneity in nonlinear panel data models.

Download or read book Fixed Effects Versus Random Effects Estimation of Dynamic Panel Data Models written by Hugo Kruiniger and published by . This book was released on 2019 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper proposes new GMM estimators for the panel AR(1) model when the ratio of the variance of the individual effects to the variance of the idiosyncratic errors is large. First, we present a necessary condition for large N, fixed T consistency of any Fixed Effects or Random Effects estimator for this model. This condition is also sufficient for consistency of the FE estimators, which only depend on differences of the data. Next we show that RE estimators can still be consistent when the data is mean-stationary and the ratio of the variances is infinite. For instance, when T>3, the 2-step optimal System estimator is consistent provided that the elements of the weight matrix are consistently estimated. We argue that the RE Quasi ML estimator can be used for this purpose. The commonly used 1-step and 2-step System estimators are inconsistent in this case. We also propose local asymptotic approximations to the distributions of RE GMM estimators that are more accurate than conventional approximations when the data are mean-stationary and the ratio of the variances is large and we discuss conditions for redundancy of the moment conditions that include levels of the data. Finally, we conduct a Monte Carlo study into the finite sample properties of various estimators and related confidence intervals, and to illustrate the usefulness of our new System estimator we revisit the growth study of Levine et al. (2000).

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 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 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.

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: