Sheep in wolf's clothing: Using the least squares criterion for quantile estimation

Arbeitspapier

Estimation of the quantile model, especially with a large data set, can be computationally burdensome. This paper proposes using the Gaussian approximation, also known as quantile coupling, to estimate a quantile model. The intuition of quantile coupling is to divide the original observations into bins with an equal number of observations, and then compute order statistics within these bins. The quantile coupling allows one to apply the standard Gaussian-based estimation and inference to the transformed data set. The resulting estimator is asymptotically normal with a parametric convergence rate. A key advantage of this method is that it is faster than the conventional check function approach, when handling a sizable data set.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Bank of Canada Working Paper ; No. 2014-24

Classification and Topics

• Classification: Wirtschaft ; Estimation: General ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
• Subject: Econometric and statistical methods

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Chen, Heng
• Event: Veröffentlichung
• (who): Bank of Canada
• (where): Ottawa
• (when): 2014

Further information

• DOI: doi:10.34989/swp-2014-24
• Handle: http://hdl.handle.net/10419/103028
• Last update: 10.03.2025, 11:41 AM CET