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 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 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 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 Fixed Effects and Random Effects Estimation of Higher Order Spatial Autoregressive Models with Spatial Autoregressive and Heteroskedastic Disturbances written by Harald Badinger and published by . This book was released on 2014 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Dynamic Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances written by Leopoldo Catania and published by . This book was released on 2016 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose a new class of models specifi cally tailored for spatio-temporal data analysis. To this end, we generalize the spatial autoregressive model with autoregressive and heteroskedastic disturbances, i.e. SARAR(1,1), by exploiting the recent advancements in Score Driven (SD) models typically used in time series econometrics. In particular, we allow for time-varying spatial autoregressive coeffi cients as well as time-varying regressor coe fficients and cross-sectional standard deviations. We report an extensive Monte Carlo simulation study in order to investigate the finite sample properties of the Maximum Likelihood estimator for the new class of models as well as its exibility in explaining several dynamic spatial dependence processes. The new proposed class of models are found to be economically preferred by rational investors through an application in portfolio optimization.

Download or read book Specification Estimation of Spatial Autoregressive Models with Autoregressive Heteroskedastic Disturbances written by and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances written by Harry H. Kelejian and published by . This book was released on 2008 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal But Heteroskedastic Disturbances written by Takahisa Yokoi and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Likelihood functions of spatial autoregressive models with normal but heteroskedastic disturbances have been already derived [Anselin (1988, ch.6)]. But there is no implementation for maximum likelihood estimation of these likelihood functions in general (heteroskedastic disturbances) cases. This is the reason why less efficient IV-based methods, 'robust 2-SLS' estimation for example, must be applied when disturbance terms may be heteroskedastic. In this paper, we develop a new computer program for maximum likelihood estimation and confirm the efficiency of our estimator in heteroskedastic disturbance cases using Monte Carlo simulations.

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 GMM Estimation of Spatial Autoregressive Stochastic Frontier Model with Heteroskedastic Inefficiency written by 彭薌芸 and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Advanced Medical Statistics 2nd Edition written by Ying Lu and published by World Scientific. This book was released on 2015-06-29 with total page 1471 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book aims to provide both comprehensive reviews of the classical methods and an introduction to new developments in medical statistics. The topics range from meta analysis, clinical trial design, causal inference, personalized medicine to machine learning and next generation sequence analysis. Since the publication of the first edition, there have been tremendous advances in biostatistics and bioinformatics. The new edition tries to cover as many important emerging areas and reflect as much progress as possible. Many distinguished scholars, who greatly advanced their research areas in statistical methodology as well as practical applications, also have revised several chapters with relevant updates and written new ones from scratch.The new edition has been divided into four sections, including, Statistical Methods in Medicine and Epidemiology, Statistical Methods in Clinical Trials, Statistical Genetics, and General Methods. To reflect the rise of modern statistical genetics as one of the most fertile research areas since the publication of the first edition, the brand new section on Statistical Genetics includes entirely new chapters reflecting the state of the art in the field.Although tightly related, all the book chapters are self-contained and can be read independently. The book chapters intend to provide a convenient launch pad for readers interested in learning a specific topic, applying the related statistical methods in their scientific research and seeking the newest references for in-depth research.

Download or read book Three Essays on Spatial Econometric Models with Missing Data written by Wei Wang and published by . This book was released on 2010 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: This dissertation is composed of three essays on spatial econometric models with missing data. Spatial models that have a long history in regional science and geography have received substantial attention in various areas of economics recently. Applications of spatial econometric models prevail in urban, developmental and labor economics among others. In practice, an issue that researchers often face is the missing data problem. Although many solutions such as list-wise deletion and EM algorithm can be found in literature, most of them are either not suited for spatial models or hard to apply due to technical difficulties. My research focuses on the estimation of the spatial econometric models in the presence of missing data problems. The first chapter develops a GMM method based on linear moments for the estimation of mixed regressive, spatial autoregressive (MRSAR) models with missing observations in the dependent variables. The estimation method uses the expectation of the missing data, as a function of the observed independent variables and the parameters to be estimated, to replace the missing data themselves in the estimation. The proposed GMM estimators are shown to be consistent and asymptotically normal. Feasible optimal weighting matrix for the GMM estimation is given. We extend our estimation method to MRSAR models with heteroskedastic disturbances, high order MRSAR models and unbalanced spatial panel data models with random effects as well. From these extensions, we see that the proposed GMM method has more compatibility, compared with the conventional EM algorithm. The second chapter considers a group interaction model first proposed by Lee (2006); this model is a special case of the spatial autoregressive (SAR) models. It is a first attempt to estimate the model in a more general random sample setting, i.e. a framework in which only a random sample rather than the whole population in a group is available. We incorporate group heteroskedasticity along with the endogenous, exogenous and group fixed effects in the model. We prove that, under some basic assumptions and certain identification conditions, the quasi maximum likelihood (QML) estimators are consistent and asymptotically normal when the functional form of the group heteroskedasticity is known. Two types of misspecifications are considered, and, under each, the estimators are inconsistent. We also propose IV estimation in the case that the group heteroskedasticity is unknown. A LM test of group heteroskedasticity is given at the end. The third chapter considers the same group interaction model as that in the second chapter, but focuses on the large group interaction case and uses a random effects setting for the group specific characters. A GMM estimation framework using moment conditions from both within and between equations is applied to the model. We prove that under some basic assumptions and certain identification conditions, the GMM estimators are consistent and asymptotically normal, and the convergence rates of the estimators are higher than those of the estimators derived from the within equations only. Feasible optimal GMM estimators are proposed.

Download or read book GM Estimation of Higher Order Spatial Autoregressive Processes in Cross Section Models with Heteroskedastic Disturbances written by and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book GM Estimation of Higher order Spatial Autoregressive Processes in Cross section Models with Heteroskedastic Disturbances written by Harald Badinger and published by . This book was released on 2008 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper generalizes the approach to estimating a first-order spatial autoregressive model with spatial autoregressive disturbances (SARAR(1,1)) in a cross-section with heteroskedastic innovations by Kelejian and Prucha (2008) to the case of spatial autoregressive models with spatial autoregressive disturbances of arbitrary (finite) order (SARAR(R,S)). We derive the moment conditions and the optimal weighting matrix for a generalized moments (GM) estimation procedure of the spatial regressive parameters of the disturbance process and define a generalized two-stages least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators, derive their (joint) asymptotic distribution, and provide Monte Carlo evidence on their small sample performance.