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Gmm Estimation Of Spatial Autoregressive Models With Autoregressive And Heteroskedastic Disturbances
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Book Synopsis GMM Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances by : Osman Dogan
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.
Book Synopsis Spatial Econometrics: Spatial Autoregressive Models by : Lung-fei Lee
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.
Book Synopsis Spatial Autoregressive Models with Unknown Heteroskedasticity by : Osman Dogan
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.
Book Synopsis GMM Estimation of Spatial Autoregressive Models with Moving Average Disturbances by : Osman Dogan
Download or read book GMM Estimation of Spatial Autoregressive Models with Moving Average Disturbances 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 paper, we introduce the one-step generalized method of moments (GMM) estimation methods considered in Lee (2007a) and Liu, Lee, and Bollinger (2010) to spatial models that impose a spatial moving average process for the disturbance term. First, we determine the set of best linear and quadratic moment functions for GMM estimation. Second, we show that the optimal GMM estimator (GMME) formulated from this set is the most efficient estimator within the class of GMMEs formulated from the set of linear and quadratic moment functions. Our analytical results show that the one-step GMME can be more efficient than the quasi maximum likelihood (QMLE), when the disturbance term is simply i.i.d. With an extensive Monte Carlo study, we compare its finite sample properties against the MLE, the QMLE and the estimators suggested in Fingleton (2008a).
Book Synopsis GMM Estimation of Spatial Autoregressive Models with Moving Average Disturbances by : Suleyman Taspinar
Download or read book GMM Estimation of Spatial Autoregressive Models with Moving Average Disturbances written by Suleyman Taspinar and published by . This book was released on 2017 with total page 63 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this paper, we introduce the one-step generalized method of moments (GMM) estimation methods considered in Lee (2007a) and Liu, Lee, and Bollinger (2010) to a spatial autoregressive model that has a spatial moving average process in the disturbance term (for short SARMA (1,1)). First, we determine the set of the best linear and quadratic moment functions for the GMM estimation. Second, we show that the GMM estimator (GMME) formulated from this set is the most efficient estimator within the class of GMMEs formulated from the set of linear and quadratic moment functions. Our analytical results show that the GMME can be asymptotically equivalent to the maximum likelihood estimator (MLE), when the disturbance term is i.i.d. Normal. When the disturbance term is simply i.i.d., the one-step GMME can be more efficient than the quasi MLE (QMLE). With an extensive Monte Carlo study, we compare its finite sample properties against the MLE, the QMLE and the estimators suggested in Fingleton (2008).
Book Synopsis Specification & Estimation of Spatial Autoregressive Models with Autoregressive & Heteroskedastic Disturbances by :
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:
Book Synopsis Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances by : Harry H. Kelejian
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:
Book Synopsis GMM Estimation of the Autoregressive Parameter in a Spatial Autoregressive Error Model Using Regression Residuals by : Matthias Arnold
Download or read book GMM Estimation of the Autoregressive Parameter in a Spatial Autoregressive Error Model Using Regression Residuals written by Matthias Arnold and published by . This book was released on 2007 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Heteroskedasticity of Unknown Form in Spatial Autoregressive Models with Moving Average Disturbance Term by : Osman Dogan
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.
Book Synopsis Efficient Maximum Likelihood Estimation of Spatial Autoregressive Models with Normal But Heteroskedastic Disturbances by : Takahisa Yokoi
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.
Book Synopsis Cross Sectional Dependence in Spatial Econometric Models by : Stefan Klotz
Download or read book Cross Sectional Dependence in Spatial Econometric Models written by Stefan Klotz and published by LIT Verlag Münster. This book was released on 2004 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is concerned with spatial dependence in econometric models, offering a work of reference to the applied researcher. In economics, spatial aspects are usually somewhat disregarded, which - as is shown and quantified here - may seriously impair research results. It presents the basic tool kit of treating cross sectional dependence, which typically occurs between spatial observations. The methods are introduced as straightforward enhancement of standard econometric models and methods, placing emphasis on the practical aspects of their features.
Book Synopsis GM Estimation of Higher-order Spatial Autoregressive Processes in Cross-section Models with Heteroskedastic Disturbances by : Harald Badinger
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.
Book Synopsis GM Estimation of Higher-Order Spatial Autoregressive Processes in Cross-Section Models with Heteroskedastic Disturbances by :
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:
Book Synopsis Estimation Methods for Spatial Autoregressive Structures by : Luc Anselin
Download or read book Estimation Methods for Spatial Autoregressive Structures written by Luc Anselin and published by . This book was released on 1980 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Essays on Theories and Applications of Spatial Econometric Models by : Xu Lin
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.
Book Synopsis Advances in Spatial Econometrics by : Luc Anselin
Download or read book Advances in Spatial Econometrics written by Luc Anselin and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: World-renowned experts in spatial statistics and spatial econometrics present the latest advances in specification and estimation of spatial econometric models. This includes information on the development of tools and software, and various applications. The text introduces new tests and estimators for spatial regression models, including discrete choice and simultaneous equation models. The performance of techniques is demonstrated through simulation results and a wide array of applications related to economic growth, international trade, knowledge externalities, population-employment dynamics, urban crime, land use, and environmental issues. An exciting new text for academics with a theoretical interest in spatial statistics and econometrics, and for practitioners looking for modern and up-to-date techniques.
Book Synopsis A Generalized Spatial Two Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances by : Harry H. Kelejian
Download or read book A Generalized Spatial Two Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances written by Harry H. Kelejian and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Cross sectional spatial models frequently contain a spatial lag of the dependent variable as a regressor, or a disturbance term which is spatially autoregressive. In this paper we describe a computationally simple procedure for estimating cross sectional models which contain both of these characteristics. We also give formal large sample results.
