The Simultaneous Spatial Autoregressive Model And Its Application In The Housing And Pharmaceutical Markets

Abstract: My research focuses on the extension of the spatial autoregressive (SAR) model into a system of simultaneous equations. The resulting new model is useful in studying problems involving multiple networks where individuals are not only linked to members of the same network but also interact with members of the other networks. The behavior of each individual is affected by the behavior of those to whom he is linked. The magnitude of such effects, which are referred to as spatial effects, depends on the strength of the links.

Databases with cross-sectional interdependent variables have highlighted the need for new data analysis techniques to model interdependence patterns cross-sectional units. Among various models to describe the interdependence, spatial autoregressive models (SAR) have attracted much attention. The theory and practice of single dependent variable SAR have been well established. Although a large number of economic theories may concern about interrelations among several economic variables, econometric studies regarding multivariate and simultaneous equations SAR models are limited. This dissertation is filling in this gap. This dissertation is composed of two chapters, the first chapter focuses on models with cross-sectional data, while the second chapter is on models in panel data which incorporates both intertemporal dynamics and spatial interdependence. The first chapter investigates a simultaneous equations spatial autoregressive model which incorporates simultaneity effects, own-variable spatial lags and cross-variable spatial lags as explanatory variables, and allows for correlation between disturbances across equations. In exposition, this chapter also discusses a multivariate spatial autoregressive model that can be treated as a reduced form of the simultaneous equations model. For a multivariate model, we provide identification conditions in terms of the existence of instruments for spatial lags and regularities of the weight matrix structure. Rank conditions and order conditions are provided for identification of structural parameters in the simultaneous equations model. In this chapter we study parameter spaces, the parameter identification, asymptotic properties of the quasi-maximum likelihood estimation, and computational issues. Monte Carlo experiments illustrate the advantages of the QML, broader applicability and efficiency, compared to instrumental variables based estimation methods in the existing literature. The second chapter introduces multivariate and simultaneous equations dynamic panel spatial autoregressive models in the cases of stability and spatial cointegration. A spatial unit is assumed to depend on its lagged term, and to respond to its neighbours' or peers' behaviour in the current period (spatial lags), and in the previous period (space-time lags). The disturbances in the model are specified with time fixed effects and individual fixed effects in addition to idiosyncratic disturbances. This chapter investigates identification for the model with simultaneous effects, time dynamic effects, and spatial effects. In the estimation of stable and spatially cointegrated models, we investigate QMLE and establish asymptotic properties of the estimator. Convergence rates of parameters may change depending on variables being stable or unstable. We analyze asymptotic biases and suggest bias-corrected estimates. We also study a robust estimation method which can be applied to stable case, spatial cointergration case and some spatial explosion cases. We apply the model to study the grain market integration using a unique historical dataset of rice and wheat prices of 65 cities in 49 years in Yangtze River Basin. The empirical result shows that rice and wheat prices are spatially cointegrated across cities. These results provide evidences of interregional and intertemporal grain market integration and trading network in the eighteenth-century Yangtze River basin.

This textbook is a comprehensive introduction to applied spatial data analysis using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcases key topics, including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github to help readers practise techniques via replication and exercises. This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data.

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

This book provides an overview of three generations of spatial econometric models: models based on cross-sectional data, static models based on spatial panels and dynamic spatial panel data models. The book not only presents different model specifications and their corresponding estimators, but also critically discusses the purposes for which these models can be used and how their results should be interpreted.

Spatial Regression Analysis Using Eigenvector Spatial Filtering provides theoretical foundations and guides practical implementation of the Moran eigenvector spatial filtering (MESF) technique. MESF is a novel and powerful spatial statistical methodology that allows spatial scientists to account for spatial autocorrelation in their georeferenced data analyses. Its appeal is in its simplicity, yet its implementation drawbacks include serious complexities associated with constructing an eigenvector spatial filter. This book discusses MESF specifications for various intermediate-level topics, including spatially varying coefficients models, (non) linear mixed models, local spatial autocorrelation, space-time models, and spatial interaction models. Spatial Regression Analysis Using Eigenvector Spatial Filtering is accompanied by sample R codes and a Windows application with illustrative datasets so that readers can replicate the examples in the book and apply the methodology to their own application projects. It also includes a Foreword by Pierre Legendre. Reviews the uses of ESF across linear regression, generalized linear regression, spatial autocorrelation measurement, and spatially varying coefficient models Includes computer code and template datasets for further modeling Provides comprehensive coverage of related concepts in spatial data analysis and spatial statistics

This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.

In the United States, some populations suffer from far greater disparities in health than others. Those disparities are caused not only by fundamental differences in health status across segments of the population, but also because of inequities in factors that impact health status, so-called determinants of health. Only part of an individual's health status depends on his or her behavior and choice; community-wide problems like poverty, unemployment, poor education, inadequate housing, poor public transportation, interpersonal violence, and decaying neighborhoods also contribute to health inequities, as well as the historic and ongoing interplay of structures, policies, and norms that shape lives. When these factors are not optimal in a community, it does not mean they are intractable: such inequities can be mitigated by social policies that can shape health in powerful ways. Communities in Action: Pathways to Health Equity seeks to delineate the causes of and the solutions to health inequities in the United States. This report focuses on what communities can do to promote health equity, what actions are needed by the many and varied stakeholders that are part of communities or support them, as well as the root causes and structural barriers that need to be overcome.