Interpretation: the final spatial frontier

2019 ◽  
pp. 1-17
Author(s):  
Guy D. Whitten ◽  
Laron K. Williams ◽  
Cameron Wimpy
Keyword(s):  
Political Science ◽  
Spatial Models ◽  
Econometric Models ◽  
Spatial Effects ◽  
Spatial Econometric ◽  
Spatial Econometric Models ◽  
Spatial Parameter ◽  
Spatial Interpretation

AbstractThe use of spatial econometric models in political science has steadily risen in recent years. However, the interpretation of these models has generally ignored the important substantive, and even spatial, nature of the estimated effects. This leaves many papers with a (non-spatial) interpretation of coefficients on the covariates and a brief discussion of the sign and strength of the spatial parameter. We introduce a general approach to interpreting spatial models and provide several avenues for an exposition of substantive spatial effects. Our approach can be generalized to most models in the spatial econometric taxonomy. Building on the example of the diffusion of democracy, we elucidate how our approach can be applied to modern political science problems.

LARGE SAMPLE PROPERTIES OF BAYESIAN ESTIMATION OF SPATIAL ECONOMETRIC MODELS

2020 ◽  
pp. 1-39
Author(s):  
Xiaoyi Han ◽  
Lung-Fei Lee ◽  
Xingbai Xu
Keyword(s):  
Bayesian Estimation ◽  
Asymptotic Properties ◽  
Spatial Models ◽  
Econometric Models ◽  
Spatial Econometric ◽  
Large Sample ◽  
Spatial Econometric Models ◽  
Posterior Probability Density ◽  
Spatial Autoregressive ◽  
Bayesian Estimators

Abstract This paper studies asymptotic properties of a posterior probability density and Bayesian estimators of spatial econometric models in the classical statistical framework. We focus on the high-order spatial autoregressive model with spatial autoregressive disturbance terms, due to a computational advantage of Bayesian estimation. We also study the asymptotic properties of Bayesian estimation of the spatial autoregressive Tobit model, as an example of nonlinear spatial models. Simulation studies show that even when the sample size is small or moderate, the posterior distribution of parameters is well approximated by a normal distribution, and Bayesian estimators have satisfactory performance, as classical large sample theory predicts.

Beyond Policy Diffusion: Spatial Econometric Models of Public Administration

2018 ◽  
Vol 29 (4) ◽  
pp. 591-608 ◽  
Author(s):  
Scott J Cook ◽  
Seung-Ho An ◽  
Nathan Favero
Keyword(s):  
Public Administration ◽  
Spatial Models ◽  
State Level ◽  
Cross Sectional Study ◽  
Econometric Models ◽  
Education Spending ◽  
Spatial Econometric ◽  
Cross Sectional ◽  
Econometric Methods ◽  
Spatial Econometric Models

Abstract Interdependence in the decision-making or behaviors of various organizations and administrators is often neglected in the study of public administration. Failing to account for such interdependence risks an incomplete understanding of the choices made by these actors and agencies. As such, we show how researchers analyzing cross-sectional or time-series-cross-sectional (TSCS) data can utilize spatial econometric methods to improve inference on existing questions and, more interestingly, engage a new set of theoretical questions. Specifically, we articulate several general mechanisms for spatial dependence that are likely to appear in research on public administration (isomorphism, competition, benchmarking, and common exposure). We then demonstrate how these mechanisms can be tested using spatial econometric models in two applications: first, a cross-sectional study of district-level bilingual education spending and, second, a TSCS analysis on state-level healthcare administration. In our presentation, we also briefly discuss many of the practical challenges confronted in estimating spatial models (e.g., weights specification, model selection, effects calculation) and offer some guidance on each.

Spatial Effect of Crime on Peer-to-Peer Lodging Performance

2021 ◽  
pp. 004728752110082
Author(s):  
Yu-Hua Xu ◽  
Lori Pennington-Gray ◽  
Jinwon Kim
Keyword(s):  
Peer To Peer ◽  
Spatial Effect ◽  
Econometric Models ◽  
Spatial Econometric ◽  
Community Based ◽  
Negatively Associated ◽  
Business Operations ◽  
Spatial Econometric Models ◽  
Crime Types ◽  
Social Uncertainty

Safety is a major factor impacting consumers’ participation in peer-to-peer (P2P) economies. Using spatial econometric models, this study examined crime effects on the performance (RevPAR) of P2P lodgings at three spatial ranges: property, community, and destination level. The performance of P2P lodgings is negatively associated with crime densities, while the degree of the association varies by crime types and room types. Crime can “spill over” to the neighborhood and have the strongest impact at the community level, followed by the destination level and the property level. The study provides a way to understand tourism risks using criminology theories and the concept of social uncertainty. Empirically, the study provides implications to the governance of community-based lodging business. We suggest that the effect of crime on P2P lodging performance was more conditioned by the safety environment in its neighborhood and the whole destination, rather than individual business operations.

Are there industrial SO2 convergences in China’s prefecture-level cities? New evidence from a spatial econometric perspective

2019 ◽  
Vol 31 (3) ◽  
pp. 440-460
Author(s):  
Yongqing Nan ◽  
Qin Li ◽  
Haiya Cai ◽  
Zhou Qin
Keyword(s):  
Sulfur Dioxide ◽  
Regional Cooperation ◽  
Estimation Methods ◽  
Kuznets Curve ◽  
Spatial Effects ◽  
Spatial Econometric ◽  
Sulfur Dioxide Emissions ◽  
Spatial Econometric Models ◽  
Per Capita ◽  
New Evidence

As the world’s largest emitter of sulfur dioxide, China is facing mounting domestic and international pressures to tackle the increasingly serious atmospheric pollution. Convergence is an important inherent characteristic of sulfur dioxide discharge. This study examines the convergence of per capita sulfur dioxide emissions across 280 Chinese prefecture-level cities from 2003 to 2016. Due to the spatial autocorrelation of air pollutants, conventional estimation methods for β convergence ignore the spatial effects and produce biased results. Consequently, spatial econometric models with different weight matrices are employed to control for spatial effects. The empirical results indicate that per capita sulfur dioxide emissions exhibit both absolute β convergence and conditional β convergence, and spatial effect and other socioeconomic factors accelerate the convergence speed. In addition, this study verifies the environmental Kuznets curve hypothesis between sulfur dioxide and gross domestic product. The results highlight the importance of regional cooperation and coordination when formulating environmental and industrial policies.

Spatial econometric models

2020 ◽  
pp. 113-158
Author(s):  
Hajime Seya ◽  
Takahiro Yoshida ◽  
Yoshiki Yamagata
Keyword(s):  
Econometric Models ◽  
Spatial Econometric ◽  
Spatial Econometric Models
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