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Common threshold in quantile regressions with an application to pricing for reputation

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  • Liangjun Su
  • Pai Xu
Abstract
The paper develops a systematic estimation and inference procedure for quantile regression models where there may exist a common threshold effect across different quantile indices. We first propose a sup-Wald test for the existence of a threshold effect, and then study the asymptotic properties of the estimators in a threshold quantile regression model under the shrinking threshold effect framework. We consider several tests for the presence of a common threshold value across different quantile indices and obtain their limiting distributions. We apply our methodology to study the pricing strategy for reputation through the use of a data set from Taobao.com. In our economic model, an online seller maximizes the sum of the profit from current sales and the possible future gain from a targeted higher reputation level. We show that the model can predict a jump in optimal pricing behavior, which is considered as “reputation effect” in this paper. The use of threshold quantile regression model allows us to identify and explore the reputation effect and its heterogeneity in data. We find both reputation effects and common thresholds for a range of quantile indices in seller’s pricing strategy in our application.

Suggested Citation

  • Liangjun Su & Pai Xu, 2019. "Common threshold in quantile regressions with an application to pricing for reputation," Econometric Reviews, Taylor & Francis Journals, vol. 38(4), pages 417-450, April.
    • Handle: RePEc:taf:emetrv:v:38:y:2019:i:4:p:417-450
    DOI: 10.1080/07474938.2017.1318469
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    Cited by:

    1. Li, Zheng & Zeng, Jingjing & Hensher, David A., 2023. "An efficient approach to structural breaks and the case of automobile gasoline consumption in Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    2. Junho Lee & Ying Sun & Huixia Judy Wang, 2021. "Spatial cluster detection with threshold quantile regression," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    3. Hong, Yanran & Li, Pan & Wang, Lu & Zhang, Yaojie, 2023. "New evidence of extreme risk transmission between financial stress and international crude oil markets," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. Martins, Luis F., 2021. "The US debt–growth nexus along the business cycle," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

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