Dirty spatial econometrics
Dirty spatial econometrics
复制标题
肮脏的空间计量经济学
DOI：
10.1007/s00168-015-0726-5
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
D. Giuliani
中科院分区：
文献类型：
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作者：
G. Arbia;G. Espa;D. Giuliani
空间数据往往受到一系列缺陷的污染，这些缺陷降低了空间数据的质量，并可能极大地扭曲基于空间计量经济模型的推论结论。标准空间计量经济学教科书中考虑的“干净”理想情况是，当我们将Cliff-Ord类型模型拟合到空间单元构成全部人口的数据时，没有缺失数据，并且没有测量和位置误差的空间观测不确定性。不幸的是，在实际情况下，现实往往是非常不同的，数据集包含各种各样的不完善之处：它们往往是基于从整个人口中抽取的样本，一些数据缺失，它们几乎总是包含属性和位置错误。这是一种“肮脏的”空间计量经济学建模的情况。本文通过一系列的Monte Carlo实验，研究了缺失数据和位置误差这两种污染源对空间计量经济模型估计和假设检验的影响。
Spatial data are often contaminated with a series of imperfections that reduce their quality and can dramatically distort the inferential conclusions based on spatial econometric modeling. A “clean” ideal situation considered in standard spatial econometrics textbooks is when we fit Cliff-Ord-type models to data where the spatial units constitute the full population, there are no missing data, and there is no uncertainty on the spatial observations that are free from measurement and locational errors. Unfortunately in practical cases the reality is often very different and the datasets contain all sorts of imperfections: They are often based on a sample drawn from the whole population, some data are missing and they almost invariably contain both attribute and locational errors. This is a situation of “dirty” spatial econometric modeling. Through a series of Monte Carlo experiments, this paper considers the effects on spatial econometric model estimation and hypothesis testing of two specific sources of dirt, namely missing data and locational errors.
使用多重插补方法的巢式病例对照研究。
DOI：
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发表时间：
2014
期刊：
影响因子：
--
作者：
M. Yoshida;J. T. Ye;Y. J. Zhang;Y. Imai;S. Kimura;A. Fujiwara;T. Nishizaki;N. Kobayashi;M. Nakano;Y. Iwasa;野間久史
通讯作者：
野間久史