Download or read book Estimation of Spatial Autoregressive Models with Dyadic Observations and Limited Dependent Variables written by Shali Luo and published by . This book was released on 2012 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial correlation, like temporal correlation, often leads to inconsistent estimates if not properly handled. This dissertation addresses spatial correlation in flow data that are recorded as binary or censored values. Flow data involve both an origin and a destination by nature, so they are subject to spatial dependence in a complicated manner. Similar to the spatial OD modeling suggested by LeSage and Pace (2008), this dissertation devises three spatial lag terms to specifically capture spatial correlation between observations on OD flows induced by a neighboring relationship between origins, between destinations, as well as a dual neighboring relationship both at the origin and the destination. The three spatial lags are incorporated into regression models with binary and censored dependent variables, respectively. However, the non-linearity of the limited dependent variable models in the presence of spatial lags makes an ML estimator inconsistent. To circumvent the inconsistent estimation, this study develops Bayesian estimation procedures for the newly proposed spatial models.

Download or read book The Estimation of Spatial Autoregressive Models with Missing Data of the Dependent Variables written by Matthias Koch and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper focuses on several estimation methods for SAR- models in case of missing observations in the dependent variable. First, we show with an example and then in general, how missing observations can change the model and thus resulting in the failure of the 'traditional' estimation methods. To estimate the SAR- model with missings we propose different estimation methods, like GMM, NLS and OLS. We will suggest to derive some of the estimators based on a model approximation. A Monte Carlo Simulation is conducted to compare the different estimation methods in their diverse numerical and sample size aspects.

Download or read book Spatial Regression Models written by Michael Don Ward and published by SAGE. This book was released on 2008-02-29 with total page 113 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assuming no prior knowledge this book is geared toward social science readers, unlike other volumes on this topic. The text illustrates concepts using well known international, comparative, and national examples of spatial regression analysis. Each example is presented alongside relevant data and code, which is also available on a Web site maintained by the authors.

Download or read book Spatial Regression Models written by Michael D. Ward and published by SAGE Publications, Incorporated. This book was released on 2018-04-30 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial Regression Models illustrates the use of spatial analysis in the social sciences within a regression framework and is accessible to readers with no prior background in spatial analysis. The text covers different modeling-related topics for continuous dependent variables, including: mapping data on spatial units, exploratory spatial data analysis, working with regression models that have spatially dependent regressors, and estimating regression models with spatially correlated error structures. Using social sciences examples based on real data, Michael D. Ward and Kristian Skrede Gleditsch illustrate the concepts discussed, and show how to obtain and interpret relevant results. The examples are presented along with the relevant code to replicate all the analysis using the R package for statistical computing. Users can download both the data and computer code to work through all the examples found in the text. New to the Second Edition is a chapter on mapping as data exploration and its role in the research process, updates to all chapters based on substantive and methodological work, as well as software updates, and information on estimation of time-series, cross-sectional spatial models.

Download or read book Introduction to Spatial Econometrics written by James LeSage and published by CRC Press. This book was released on 2009-01-20 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observat

Download or read book New Directions in Spatial Econometrics written by Luc Anselin and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: The promising new directions for research and applications described here include alternative model specifications, estimators and tests for regression models and new perspectives on dealing with spatial effects in models with limited dependent variables and space-time data.

Download or read book Spatial Econometrics using Microdata written by Jean Dubé and published by John Wiley & Sons. This book was released on 2014-11-10 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to spatial analyses concerning disaggregated (or micro) spatial data. Particular emphasis is put on spatial data compilation and the structuring of the connections between the observations. Descriptive analysis methods of spatial data are presented in order to identify and measure the spatial, global and local dependency. The authors then focus on autoregressive spatial models, to control the problem of spatial dependency between the residues of a basic linear statistical model, thereby contravening one of the basic hypotheses of the ordinary least squares approach. This book is a popularized reference for students looking to work with spatialized data, but who do not have the advanced statistical theoretical basics.

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:

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.

Download or read book Spatial AutoRegression SAR Model written by Baris M. Kazar and published by Springer Science & Business Media. This book was released on 2012-03-02 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explosive growth in the size of spatial databases has highlighted the need for spatial data mining techniques to mine the interesting but implicit spatial patterns within these large databases. This book explores computational structure of the exact and approximate spatial autoregression (SAR) model solutions. Estimation of the parameters of the SAR model using Maximum Likelihood (ML) theory is computationally very expensive because of the need to compute the logarithm of the determinant (log-det) of a large matrix in the log-likelihood function. The second part of the book introduces theory on SAR model solutions. The third part of the book applies parallel processing techniques to the exact SAR model solutions. Parallel formulations of the SAR model parameter estimation procedure based on ML theory are probed using data parallelism with load-balancing techniques. Although this parallel implementation showed scalability up to eight processors, the exact SAR model solution still suffers from high computational complexity and memory requirements. These limitations have led the book to investigate serial and parallel approximate solutions for SAR model parameter estimation. In the fourth and fifth parts of the book, two candidate approximate-semi-sparse solutions of the SAR model based on Taylor's Series expansion and Chebyshev Polynomials are presented. Experiments show that the differences between exact and approximate SAR parameter estimates have no significant effect on the prediction accuracy. In the last part of the book, we developed a new ML based approximate SAR model solution and its variants in the next part of the thesis. The new approximate SAR model solution is called the Gauss-Lanczos approximated SAR model solution. We algebraically rank the error of the Chebyshev Polynomial approximation, Taylor's Series approximation and the Gauss-Lanczos approximation to the solution of the SAR model and its variants. In other words, we established a novel relationship between the error in the log-det term, which is the approximated term in the concentrated log-likelihood function and the error in estimating the SAR parameter for all of the approximate SAR model solutions.

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 Semiparametric Estimation of Censored Spatial Autoregressive Models written by Tadao Hoshino and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This study considers the estimation of spatial autoregressive models with censored dependent variables, where the spatial autocorrelation exists within the uncensored latent dependent variables. The estimator proposed in this paper is semiparametric, in the sense that the error distribution is not parametrically specified and can be heteroscedastic. Under a median restriction, we show that the proposed estimator is consistent and asymptotically normally distributed. As an empirical illustration, we investigate the determinants of the risk of assault and other violent crimes including injury in the Tokyo metropolitan area.

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 Spatial Autocorrelation written by John Odland and published by SAGE Publications, Incorporated. This book was released on 1988-03 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autocorrelation occurs whenever a variable exhibits a regular pattern over space, when its values at a set of locations depend on values of the same variables at other locations.Odland introduces spatial autocorrelation to the reader in a concise and readable fashion, and describes the statistical p.

Download or read book Smoothed Spatial Maximum Score Estimation of Spatial Autoregressive Binary Choice Panel Models written by Jinghua Lei and published by . This book was released on 2013 with total page 36 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robust Estimation of Multivariate and Spatial Autoregression written by Mohammad Roknossadati and published by . This book was released on 2008 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models written by Emir Malikov and published by . This book was released on 2017 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper considers a flexible semiparametric spatial autoregressive (mixed-regressive) model in which unknown coefficients are permitted to be nonparametric functions of some contextual variables to allow for potential nonlinearities and parameter heterogeneity in the spatial relationship. Unlike other semiparametric spatial dependence models, ours permits the spatial autoregressive parameter to meaningfully vary across units and thus allows the identification of a neighborhood-specific spatial dependence measure conditional on the vector of contextual variables. We propose several (locally) nonparametric GMM estimators for our model. The developed two-stage estimators incorporate both the linear and quadratic orthogonality conditions and are capable of accommodating a variety of data generating processes, including the instance of a pure spatially autoregressive semiparametric model with no relevant regressors as well as multiple partially linear specifications. All proposed estimators are shown to be consistent and asymptotically normal. We also contribute to the literature by putting forward two test statistics to test for parameter constancy in our model. Both tests are consistent.