基于空间回归的外源性和内源性空间相互作用模型规范

J. LeSage, M. Fischer
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引用次数: 27

摘要

空间交互模型代表了一类用于模拟始发-目的地流数据的模型。本文的重点是该模型的对数正态版本。在此背景下,我们考虑了可用于容纳两种依赖情景的空间计量规范,一种涉及内源性相互作用,另一种涉及外源性相互作用。这些模型规范用允许两种不同类型的空间依赖程度的形式化方法取代了传统的起源-目的地流之间独立的假设。内源性相互作用反映了对来自邻近起源和目的地地区的流量大小反馈的反应。这种类型的相互作用可以使用LeSage和Pace(2008)提出的规范来建模,他们使用因变量的空间滞后来量化反馈效应的大小和程度,因此称为内生相互作用。外生相互作用代表了一种情况,即溢出效应来自附近(甚至可能是遥远)地区,当建模观察到跨区域网络的流动变化时,需要考虑到这些因素。与内生相互作用相反,这些背景效应不会对溢出效应产生反应,从而导致可以在不考虑流动系统长期平衡状态变化的情况下解释模型规范。就像在社会网络中一样,背景效应是使用代表邻近(或更普遍的连接)区域特征的解释变量的空间滞后来建模的,但不是因变量的空间滞后,因此称为外生相互作用。除了提出Thomas-Agnan和LeSage(2014)提出的非空间和内生空间相互作用模型的真偏导数表达式以及相关的标量汇总测度外,我们还为本文介绍的外生空间相互作用规范提出了新的标量汇总测度。一个插图将外生空间相互作用模型应用于佛罗里达州67个学区之间教师运动的流动矩阵。
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Spatial Regression-Based Model Specifications for Exogenous and Endogenous Spatial Interaction
Spatial interaction models represent a class of models that are used for modelling origin-destination flow data. The focus of this paper is on the log-normal version of the model. In this context, we consider spatial econometric specifications that can be used to accommodate two types of dependence scenarios, one involving endogenous interaction and the other exogenous interaction. These model specifications replace the conventional assumption of independence between origin-destination flows with formal approaches that allow for two different types of spatial dependence in magnitudes. Endogenous interaction reflects situations where there is a reaction to feedback regarding flow magnitudes from regions neighbouring origin and destination regions. This type of interaction can be modelled using specifications proposed by LeSage and Pace (2008) who use spatial lags of the dependent variable to quantify the magnitude and extent of the feedback effects, hence the term endogenous interaction. Exogenous interaction represents a situation where spillovers arise from nearby (or perhaps even distant) regions, and these need to be taken into account when modelling observed variations in flows across the network of regions. In contrast to endogenous interaction, these contextual effects do not generate reactions to the spillovers, leading to a model specification that can be interpreted without considering changes in the long-run equilibrium state of the system of flows. As in the case of social networks, contextual effects are modelled using spatial lags of the explanatory variables that represent characteristics of neighbouring (or more generally connected) regions, but not spatial lags of the dependent variable, hence the term exogenous interaction. In addition to setting forth expressions for the true partial derivatives of non-spatial and endogenous spatial interaction models and associated scalar summary measures from Thomas-Agnan and LeSage (2014), we propose new scalar summary measures for the exogenous spatial interaction specification introduced here. An illustration applies the exogenous spatial interaction model to a flow matrix of teacher movements between 67 school districts in the state of Florida.
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