Download or read book Heteroskedasticity Consistent Covariance Matrix Estimators for Spatial Autoregressive Models written by Suleyman Taspinar and published by . This book was released on 2018 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the presence of heteroskedasticity, conventional test statistics based on the ordinary least square estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. In this paper, we investigate the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of hetereoskedasticity. In particular, we develop various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. Our Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.

Download or read book Spatial Heteroskedasticity and Autocorrelation Consistent Estimation of Covariance Matrix written by Yixiao Sun and published by . This book was released on 2010 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers spatial heteroskedasticity and autocorrelation consistent (spatial HAC) estimation of covariance matrices of parameter estimators. We generalize the spatial HAC estimators introduced by Kelejian and Prucha (2007) to apply to linear and nonlinear spatial models with moment conditions. We establish its consistency, rate of convergence and asymptotic truncated mean squared error (MSE). Based on the asymptotic truncated MSE criterion, we derive the optimal bandwidth parameter and suggest its data dependent estimation procedure using a parametric plug-in method. The finite sample performances of the spatial HAC estimator are evaluated via Monte Carlo simulation.

Download or read book Another Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator written by Kenneth D. West and published by . This book was released on 1995 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: A þT consistent estimator of a heteroskedasticity and autocorrelation consistent covariance matrix estimator is proposed and evaluated. The relevant applications are ones in which the regression disturbance follows a moving average process of known order. In a system of þ equations, this `MA-þ' estimator entails estimation of the moving average coefficients of an þ-dimensional vector. Simulations indicate that the MA-þ estimator's finite sample performance is better than that of the estimators of Andrews and Monahan (1992) and Newey and West (1994) when cross-products of instruments and disturbances are sharply negatively autocorrelated, comparable or slightly worse otherwise.

Download or read book GMM Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances written by Osman Dogan and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a spatial econometric model containing a spatial lag in the dependent variable and the disturbance term with an unknown form of heteroskedasticity in innovations. We first prove that the maximum likelihood (ML) estimator for spatial autoregressive models is generally inconsistent when heteroskedasticity is not taken into account in the estimation. We show that the necessary condition for the consistency of the ML estimator of spatial autoregressive parameters depends on the structure of the spatial weight matrices. Then, we extend the robust generalized method of moment (GMM) estimation approach in Lin and Lee (2010) for the spatial model allowing for a spatial lag not only in the dependent variable but also in the disturbance term. We show the consistency of the robust GMM estimator and determine its asymptotic distribution. Finally, through a comprehensive Monte Carlo simulation, we compare finite sample properties of the robust GMM estimator with other estimators proposed in the literature.

Download or read book Spatial Autoregressive Models with Unknown Heteroskedasticity written by Osman Dogan and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most of the estimators suggested for the estimation of spatial autoregressive models are generally inconsistent in the presence of an unknown form of heteroskedasticity in the disturbance term. The estimators formulated from the generalized method of moments (GMM) and the Bayesian Markov Chain Monte Carlo (MCMC) frameworks can be robust to unknown forms of heteroskedasticity. In this study, the finite sample properties of the robust GMM estimator are compared with the estimators based on the Bayesian MCMC approach for the spatial autoregressive models with heteroskedasticity of an unknown form. A Monte Carlo simulation study provides evaluation of the performance of the heteroskedasticity robust estimators. Our results indicate that the MLE and the Bayesian estimators impose relatively greater bias on the spatial autoregressive parameter when there is negative spatial dependence in the model. In terms of finite sample efficiency, the Bayesian estimators perform better than the robust GMM estimator. In addition, two empirical applications are provided to evaluate relative performance of heteroskedasticity robust estimators.

Download or read book Essays on Theories and Applications of Spatial Econometric Models written by Xu Lin and published by . This book was released on 2006 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: As an effective method in analyzing interdependence among the observations, the spatial autoregressive (SAR) models have witnessed ever-increasing applications. This dissertation intends to enrich both the spatial econometrics theory and the social interaction estimations. In the first essay, a SAR model with group unobservables is applied to analyze peer effects in student academic achievement. Unlike the linear-in-means model in Manski (1993), the SAR model can identify both endogenous and contextual social effects due to variations in the peer measurements, thus resolving the "reflection problem". The group fixed effects term captures the confounding effects of the common variables faced by the same group members. I use datasets from the National Longitudinal Study of Adolescent Health (Add Health) survey and specify peer groups as friendship networks. I find evidence for both endogenous and contextual effects, even after controlling for school-grade fixed effects. The result indicates that students benefit from the presence of high quality peers, and that associating with peers living with both parents helps improve a student's GPA, while associating with peers whose mothers receive welfare has a negative effect. The second essay considers the GMM estimation of SAR models with unknown heteroskedasticity. We show that MLE is inconsistent whereas GMM estimators obtained from certain moment conditions are robust. Asymptotically valid inferences can be drawn from the consistent covariance matrix estimator. And efficiency can be improved by constructing the optimal weighted GMM estimation. We also propose some general tests for heteroskedasticity. In the Monte Carlo study, 2SLS estimators have large variances and biases in finite samples for cases where regressors do not have strong effects. The robust GMM estimator has desirable properties while the biases associated with MLE and non-robust GMM estimator may remain in large sample, especially, for the spatial effect coefficient and the intercept term. However, the magnitudes of biases are only moderate and those biases may be statistically insignificant with moderate large sample sizes. The various approaches are applied to the study of county teenage pregnancy rates. The results suggest a strong spatial convergence among county teenage pregnancy rates with a significant spatial effect.

Download or read book Spatial Econometrics written by Giuseppe Arbia and published by Springer Science & Business Media. This book was released on 2008-11-14 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial Econometrics is a rapidly evolving field born from the joint efforts of economists, statisticians, econometricians and regional scientists. The book provides the reader with a broad view of the topic by including both methodological and application papers. Indeed the application papers relate to a number of diverse scientific fields ranging from hedonic models of house pricing to demography, from health care to regional economics, from the analysis of R&D spillovers to the study of retail market spatial characteristics. Particular emphasis is given to regional economic applications of spatial econometrics methods with a number of contributions specifically focused on the spatial concentration of economic activities and agglomeration, regional paths of economic growth, regional convergence of income and productivity and the evolution of regional employment. Most of the papers appearing in this book were solicited from the International Workshop on Spatial Econometrics and Statistics held in Rome (Italy) in 2006.

Download or read book A Note on the Eicker White Heteroskedasticity consistent Covariance Matrix Estimator written by Naorayex K. Dastoor and published by . This book was released on 1993 with total page 7 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Covariance Matrix Estimation with Data dependent VAR Prewhitening Order written by Wouter J. Den Haan and published by . This book was released on 2000 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper analyzes the performance of heteroskedasticity-and-autocorrelation-consistent (HAC) covariance matrix estimators in which the residuals are prewhitened using a vector autoregressive (VAR) filter. We highlight the pitfalls of using an arbitrarily fixed lag order for the VAR filter, and we demonstrate the benefits of using a model selection criterion (either AIC or BIC) to determine its lag structure. Furthermore, once data-dependent VAR prewhitening has been utilized, we find negligible or even counter-productive effects of applying standard kernel-based methods to the prewhitened residuals; that is, the performance of the prewhitened kernel estimator is virtually indistinguishable from that of the VARHAC estimator.

Download or read book An Improved Heteroskedasticity consistent Covariance Matrix Estimator written by Francisco Cribari Neto and published by . This book was released on 1999 with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book The Bias of the Heteroskedasticity Consistent Covariance Matrix Estimator written by Andrew Chesher and published by . This book was released on 1986 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spatial Econometrics Spatial Autoregressive Models written by Lung-fei Lee and published by World Scientific. This book was released on 2023-10-16 with total page 894 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the most recently developed book in Spatial Econometrics which cover important models and estimation methods. Its coverage is rather broad, and some of the topics covered have only been developed in the recent econometric literature in spatial econometrics.The book summarizes our devoted efforts on spatial econometrics that represent joint contributions with former PhD advisees from the Ohio State University in Columbus, Ohio, USA.The coverage is comprehensive and there are a total of sixteen chapters from basic statistics and statistical theory of linear-quadratic forms, law of large numbers (LLN) and central limit theory (CLT) on martingales to nonlinear spatial mixing and spatial near-epoch dependence theories, which can justify the statistic inferences for various spatial models and their estimation. New estimation and testing approaches in empirical likelihood and general empirical likelihood, and Bootstrapping are presented. Model selection is also discussed in this book. In addition to the popular spatial autoregressive models, there are chapters on multivariate SAR models, simultaneous SAR models, and panel dynamic spatial models. Recent econometric developments on intertemporal spatial models with rational expectations and flows data in trade theory will also be included. In terms of statistics, classical estimation, testing and inference are the main concerns, and we provide classical inference for the justification of Bayesian simulation approaches.

Download or read book Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term written by Osman Dogan and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this study, I investigate the necessary condition for consistency of the maximum likelihood estimator (MLE) of spatial models with a spatial moving average process in the disturbance term. I show that the MLE of spatial autoregressive and spatial moving average parameters is generally inconsistent when heteroskedasticity is not considered in the estimation. I also show that the MLE of parameters of exogenous variables is inconsistent and determine its asymptotic bias. I provide simulation results to evaluate the performance of the MLE. The simulation results indicate that the MLE imposes a substantial amount of bias on both autoregressive and moving average parameters.

Download or read book Handbook of Applied Economic Statistics written by Aman Ullah and published by CRC Press. This book was released on 1998-02-03 with total page 646 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work examines theoretical issues, as well as practical developments in statistical inference related to econometric models and analysis. This work offers discussions on such areas as the function of statistics in aggregation, income inequality, poverty, health, spatial econometrics, panel and survey data, bootstrapping and time series.

Download or read book Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation written by Masayuki Hirukawa and published by . This book was released on 2004 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Heteroskedasticity consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity written by Halbert White and published by . This book was released on 1980 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book A Modified Heteroskedasticity Consistent Covariance Matrix Estimator with Improved Finite Sample Properties written by James G. MacKinnon and published by Kingston, Ont. : Institute for Economic Research, Queen's University. This book was released on 1983 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: