空间框架中的MARS算法:享乐模型中的非线性和空间效应

F. López, K. Kholodilin
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引用次数: 1

摘要

多变量自适应样条回归(MARS)是一种简单而强大的非参数技术,可以自动选择回归模型中的非线性项。非线性和空间效应是众多空间享乐定价模型的自然特征。在本文中,我们建议将MARS数据驱动方法与工具变量方法相结合,以解释享乐模型中潜在的非线性和空间效应。利用汉堡6000多套住宅和圣彼得堡17000多套住宅的大型数据集,我们证实了这两种效应(非线性和空间自相关)的存在。结果还表明,相邻房屋的价格对每套房屋的价格存在非线性影响。相邻房屋的高价格对房价的影响要小于相邻房屋的低价格。最后,一项广泛的蒙特卡罗练习评估了MARS将正确的空间溢出项同时纳入空间回归模型的能力,同时包括非线性效应。
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The MARS Algorithm in the Spatial Framework: Non-Linearities and Spatial Effects in Hedonic Models
Multivariate Adaptive Regression Spline (MARS) is a simple and powerful non-parametric technique that automatizes the selection of non-linear terms in regression models. Non-linearities and spatial effects are natural characteristics in numerous spatial hedonic pricing models. In this paper, we propose using the MARS data-driven methodology combined with the Instrumental Variables method in order to account for potential non-linearities and spatial effects in hedonic models. Using a large data set of more than 6,000 dwellings in Hamburg and about 17,000 in St. Petersburg, we confirm the presence of both effects (non-linearities and spatial autocorrelation). The results also show that there is a non-linear effect of the prices of neighboring houses on the price of each house. High prices for neighboring houses have a lower impact on the house price than low prices of neighboring houses. Finally, an extensive Monte Carlo exercise evaluates the ability of MARS to incorporate the correct spatial spillover terms in spatial regression models simultaneously including at same time non-linear effects.
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