关于空间计量相关问题，各位学者可以参看如下文章：1.空间计量经济学最新进展和理论框架，2.空间和时间的计量，关注二位国人，3.空间计量模型选择、估计、权重、检验，4.空间计量百科全书式的使用指南的do file公开，5.空间计量百科全书式的使用指南, 只此一份掌握此独门秘籍，6.空间计量的46页Notes, 区经相关学者可参阅，7.空间计量软件代码资源集锦(Matlab/R/Python/SAS/Stata)，8.用R语言做空间计量, 绝不容错过的简明教程，9.R软件中的空间计量经济学程序包纵览，10.空间计量的研究领域模型, 发展阶段与最新进展，11.空间计量和交互项如何使用, 将空间计量进行到底，12.JEL上空间经济学综述, 从中可以学到什么？13.空间DID双重差分方法的文献, spatial DID，14.中国所有地级市各类空间权重矩阵数据release，15.中国省级三大空间权重矩阵(相邻, 距离和经济)数据release，16.ArcGIS操作实例视频教程38讲全集，17.Arcgis使用通行Textbook推荐, 系统学习的宝库，18.Anselin讲解空间计量和GeoDa的运用，19.空间计量经济学与操作命令分解，20.空间面板数据模型估计数据, 程序和解读。
而关于DID双重差分法，各位学者可以参阅如下文章：1.DID运用经典文献，强制性许可:来自对敌贸易法的证据，2.连续DID经典文献, 土豆成就了旧世界的文明，3.截面数据DID讲述, 截面做双重差分政策评估的范式，4.RDD经典文献, RDD模型有效性稳健性检验，5.事件研究法用于DID的经典文献"环境规制"论文数据和程序，6.广义DID方法运用得非常经典的JHE文献，7.DID的经典文献"强制许可"论文数据和do程序，8.传销活动对经济发展影响, AER上截面数据分析经典文，9.多期DID的经典文献big bad banks数据和do文件，10.因果推断IV方法经典文献，究竟是制度还是人力资本促进了经济的发展？，11.AER上因果关系确立, 敏感性检验, 异质性分析和跨数据使用经典文章，12.第二篇因果推断经典，工作中断对工人随后生产效率的影响？，13.密度经济学:来自柏林墙的自然实验, 最佳Econometrica论文，14.AER上以DID, DDD为识别策略的劳动和健康经济学，15.一个使用截面数据的政策评估方法, 也可以发AER，16.多期DID模型的经典文献，big bad banks讲解","，17.多期DID的经典文献big bad banks数据和do文件，18.非线性DID, 双重变换模型CIC, 分位数DID，19.模糊(Fuzzy)DID是什么？如何用数据实现呢？20.多期DID的big bad banks中文翻译版本及各细节讲解，21.DID中行业/区域与时间趋势的交互项, 共同趋势检验, 动态政策效应检验等，22.截面数据DID操作程序指南, 一步一步教你做，23.DID的研究动态和政策评估中应用的文献综述，24.连续DID经典文献, 土豆成就了旧世界的文明，25.DID双重差分方法, 一些容易出错的地方，26.连续DID, DDD和比例DID, 不可观测选择偏差，27.加权DID, IPW-DID实证程序百科全书式的宝典，28.DID和DDD, 一个简明介绍, 双重和三重差分模型，29.DID过程中总结的地图展示技巧，30.DID的平行趋势假定检验程序和coefplot的其他用法。
Sugarcane production represents around 10% of the agricultural area and 1% of GDP in Brazil, and has grown substantially in recent years. The traditional harvest method involves burning the field to facilitate access to the canes, resulting in well-documented negative effects on health. The existing studies do not consider the effects on health in the surrounding areas. This article presents a new variety of a spatial diff-in-diff model to control for the effects of sugarcane production in neighboring non-producing regions. This method is an addition to the Spatial Econometrics literature, as it includes spatial effects on treated and untreated regions, so that the effects on both producing and surrounding non-producing regions can be properly estimated. The results indicate that the effects on the producing regions are 78% larger than if the effects on the surrounding areas were ignored. Moreover, the effects on the surrounding areas, typically ignored in other studies, are relevant, and almost as large as the effects on the producing areas. We consider treatment effect estimation via a difference-in-difference approach for spatial data with local spatial interaction such that the potential outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assumptions (common trend and ignorability) a straightforward spatially explicit version of the benchmark difference-in-differences regression is capable of identifying both direct and indirect treatment effects. We demonstrate the finite sample performance of our spatial estimator via Monte Carlo simulations. This study uses the opening of the new Circle Line (CCL) in Singapore as a natural experiment to test the effects of urban rail transit networks on non-landed private housing values. We use a network distance measure and a local-polynomial-regression approach to identify the CCL impact zone with discontinuity in housing price gradient between a treatment zone and a control zone. We then estimate the spatial difference-in-differences models that account for spatial autocorrelation in housing price changes in the two zones “before and after” the opening of the CCL. We find that the opening of the CCL increases housing value in the treated neighborhoods located within the 600-metre network distance from the new CCL stations by approximately 8.6%, relative to other properties in the untreated neighborhoods controlling for heterogeneities in housing attributes and local amenities, and spatial and temporal fixed effects. We find significant “anticipation” effects as early as 1 year prior to the opening of the CCL line, but the effects diminish closer to the actual opening date. The results imply that the inter-dependent spatial structure between the treated and the untreated neighborhoods, if neglected, may lead to over-estimation of the capitalization effects of the new transit lines on housing values. Evaluating the impact of public mass transit systems on real-estate values is an important application of the hedonic price model (HPM). Recently, a mathematical transformation of this approach has been proposed to account for the potential omission of latent spatial variables that may overestimate the impact of accessibility to mass transit systems on values. The development of a Difference-in-Differences (DID) estimator, based on the repeat-sales approach, is a move in the right direction. However, such an estimator neglects the possibility that specification of the price equation may follow a spatial autoregressive process with respect to the dependent variable. The objective of this paper is to propose a spatial Difference-in-Differences (SDID) estimator accounting for possible spatial spillover effects. Particular emphasis is placed on the development of a suitable weights matrix accounting for spatial links between observations. Finally, an empirical application of the SDID estimator based on the development of a new commuter rail transit system for the suburban agglomeration of Montréal (Canada) is presented and compared to the usual DID estimator. This paper empirically measures the potential spillover effects of big-box retail entry on the productivity of incumbent retailers in the entry regions, and investigates whether the effects differ depending on 1) if the entry is in a rural or urban area, and 2) if the incumbent retailers are within retail industries selling substitute or complement goods to those found in IKEA. To identify the IKEA-entry effect, a difference-in-difference model is suitable, but traditionally such estimators neglect the possibility that firms’ sales are determined by a process with spatially interactive responses. If ignored, these responses may cause biased estimates of the IKEA entry effect due to spatial heterogeneity of the treatment effect. One objective of this paper is thus to propose a spatial difference-in-difference estimator accounting for possible spatial spillover effects of IKEA entry. Particular emphasis is placed on the development of a suitable weight matrix accounting for the spatial links between firms, where we allow for local spatial interactions such that the outcome of observed units depends both on their own treatment as well as on the treatment of their neighbors. Our results show that for complementary goods retailers (or one-stop shopping retailers) in Haparanda and Kalmar, productivity increased by 35% and 18%, respectively, due to IKEA entry. No statistically significant effects were found for the entries in Karlstad and Gothenburg, indicating that it is mainly incumbents in smaller entry regions that benefit from IKEA entry. Also, for incumbent retailers selling substitute (or comparison shopping) goods no significant effects were found in any of the entry regions, indicating that it is mainly retailers selling complementary goods that benefit from IKEA entry. Finally, our results also show that ignoring the possibility of spatially correlated treatment effects in the regression models reduces the estimated impact of the IKEA entries in Haparanda and Kalmar on productivity in one-stop shopping retail firms with 3% and 0.1% points, respectively. This article details the use of a spatial difference-in-differences approach for measuring the effect of a vacant land greening program in Philadelphia, Pennsylvania, on nearby property values. Vacant land is a ubiquitous problem in U.S. cities, and many have recently begun to explore greening programs as an interim management strategy for vacant lots, in the hopes they will reduce the negative influence of vacancy and help to spur neighborhood development. The methods used here draw on previous approaches to modeling effects of greening on property values but expand on them to explore means of incorporating spatial relationships and allowing for spatial nonstationarity, in which the process being modeled changes across space. Spatial methods were used not only to derive data and choose appropriate observations but also to compare global and local versions of the analysis to assess spatial patterns and differences in outcomes across the study area, ultimately showing that, although greening vacant land increases surrounding property values, it does not do so uniformly across urban neighborhoods. Vacant land is a serious problem in many cities, and cities have recently begun to explore greening as a management strategy to reduce the negative influence of vacancy. The city of Philadelphia, Pennsylvania pioneered the use of a simple greening treatment?removal of debris coupled with planting grass and trees?as a means of improving blighted communities. Though they are becoming more popular, the actual economic impact of these programs is not well understood. This paper details the use of a spatial difference-in-differences approach for measuring the impact of Philadelphia's innovative vacant land greening program on nearby residential property values. This approach compares observed changes in property values surrounding treated vacant lots with observed changes around control lots?lots which might have been treated but were not. While property values throughout the city increased during the study period, properties surrounding greened vacant lots had a greater increase in value than properties surrounding nongreened vacant lots. By developing both global and local versions of the model, we also explore spatial variation in the impacts of the program?offering insight into which kinds of neighborhoods might derive the greatest economic benefit from vacant land greening programs. Empirical work in regional science has seen a growing interest in causal inference, leveraging insights from econometrics, statistics, and related fields. This has resulted in several conceptual as well as empirical papers. However, the role of spatial effects, such as spatial dependence (SD) and spatial heterogeneity (SH), is less well understood in this context. Such spatial effects violate the so-called stable unit treatment value assumption advanced by Rubin as part of the foundational framework for empirical treatment effect analysis. In this article, we consider the role of spatial effects more closely. We provide a brief overview of a number of attempts to extend existing econometric treatment effect evaluation methods with an accounting for spatial aspects and outline and illustrate an alternative approach. Specifically, we propose a spatially explicit counterfactual framework that leverages spatial panel econometrics to account for both SD and SH in treatment choice, treatment variation, and treatment effects. We illustrate this framework with a replication of a well-known treatment effect analysis, that is, the evaluation effect of minimum legal drinking age laws on mortality for US states during the period 1970?1984, a classic textbook example of applied causal inference. We replicate the results available in the literature and compare these to a range of alternative specifications that incorporate spatial effects. Today’s investment decisions in large-scale onshore wind projects in Germany are no longer determined only by the investment’s economic benefit, but also by concerns associated to social acceptance. Despite a mostly positive attitude towards the expansion of wind power, local public concerns often stem from the belief that the proximity to large-scale wind farms may lead to a decrease in property prices. In particular, the change in landscape caused by the construction of a wind farm may have an adverse impact on the view from some properties, and thus may negatively affect their price. To investigate the potential devaluation of properties in Germany due to wind farms, we use a quasi-experimental technique and apply a spatial difference-in-differences approach to various wind farm sites in the federal state of North Rhine-Westphalia. We adopt a quantitative visual impact assessment approach to account for the adverse environmental effects caused by the wind turbines. To properly account for spatial dependence and unobserved variables biases, we apply augmented spatial econometric models. The estimates indicate that the asking price for properties whose view was strongly affected by the construction of wind turbines decreased by about 10-17%. In contrast, properties with a minor or marginal view on the wind turbines experienced no devaluation. Today's investment decisions in large-scale onshore wind projects in Germany are no longer determined only by the investment's economic benefit, but also by concerns associated to social acceptance. Despite a mostly positive attitude towards the expansion of wind power, local public concerns often stem from the belief that the proximity to large-scale wind farms may lead to a decrease in property prices. In particular, the change in landscape caused by the construction of a wind farm may have an adverse impact on the view from some properties, and thus may negatively affect their price. To investigate the potential devaluation of properties in Germany due to wind farms, we use a quasi-experimental technique and apply a spatial difference-in-differences approach to various wind farm sites in the federal state of North Rhine-Westphalia. We adopt a quantitative visual impact assessment approach to account for the adverse environmental effects caused by the wind turbines. To properly account for spatial dependence and unobserved variables biases, we apply augmented spatial econometric models. The estimates indicate that the asking price for properties whose view was strongly affected by the construction of wind turbines decreased by about 9–14%. In contrast, properties with a minor or marginal view on the wind turbines experienced no devaluation.