Spatial unconditional quantile regression: application to Japanese parking price data

Hajime Seya (), Kay W. Axhausen () and Makoto Chikaraishi ()
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Hajime Seya: Kobe University
Kay W. Axhausen: ETH Zürich
Makoto Chikaraishi: Hiroshima University

The Annals of Regional Science, 2020, vol. 65, issue 2, No 4, 402 pages

Abstract: Abstract 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.

JEL-codes: C21 R12 R41 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s00168-020-00987-3

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