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 via the use of a dataset 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.
Common threshold effect, Pricing strategy, Regime change, Specification test, Threshold quantile regression
Taylor & Francis: STM, Behavioural Science and Public Health Titles
SU, Liangjun; XU, Pai; and JU, Heng.
Common threshold in quantile regressions with an application to pricing for reputation. (2017). Econometric Reviews. 1-37. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/2100
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.