分组数据迁移流的概率模型:一个理论注释

C. Chasco, P. Aroca, L. Anselin
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引用次数: 5

摘要

在这篇理论笔记中,我们提出GProbit模型作为重力模型的替代方案来估计分组数据流。这是一个基于随机效用理论的模型,符合人口行为原理。流动的GProbit模型的因变量由许多观察到的比例组成,而不是移民数量。它可以解释从任何起点迁移到目的地的倾向,这是一个有趣的相对概念,不受规模效应的影响。由于这个原因,预计它将有更好的适合性和更少的非正常问题,如西班牙地区内部移民流动的应用程序所示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Probit Models for Grouped-data Migration Flows: A Theoretical Note
In this theoretical note, we propose the GProbit model as an alternative to gravity models to esti-mate grouped-data flows. This is a model based on the random utility theory, which is consistentwith the principle of population behavior. Instead of migrant counts, the dependent variable ofthe GProbit model of flows consists of a number of observed proportions. It allows explaining thepropensity to migrate from any origin to a destination, which is an interesting relative conceptnot affected by the size effect. For this reason, it is expected to have better fit and less problemsof non-normality, as illustrated by an application for the internal migration flows of the Spanishregions.
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