Spatial unconditional quantile regression: application to Japanese parking price data
Spatial unconditional quantile regression: application to Japanese parking price data
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DOI：
10.1007/s00168-020-00987-3
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
H. Seya;K. Axhausen;M. Chikaraishi
中科院分区：
文献类型：
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作者：
H. Seya;K. Axhausen;M. Chikaraishi
本研究通过扩展Firpo等人的方法，发展了一个空间无条件分位数回归。s（Econometrica 77：953-973，2009）的无条件分位数回归，实证研究了日本不同分位数下停车价格的决定因素。实证结果表明，单位价格和单位时间的空间竞争在决定停车价格方面发挥了重要作用。相反，价格不受需求的影响，通过采用几个就业密度变量和从手机获得的汇总人流数据来近似。此外，影响停车价格的因素在白天和夜间以及无条件分位数之间存在显著差异。
The present study develops a spatial unconditional quantile regression by extending Firpo et al.’s (Econometrica 77:953–973, 2009) unconditional quantile regression and empirically investigates the determinants of parking prices at different quantiles of prices in Japan. The empirical results suggest that spatial competition in terms of unit price and the unit time play important roles in determining parking prices. On the contrary, price is unaffected by demand, approximated by adopting several employment density variables and aggregated people flow data obtained from cell phones. Besides, significant differences exist among the factors that affect parking prices during the day and at night as well as among the unconditional quantiles.