Averaging of moment condition estimators

Chen, Xiaohong; Jacho-Chávez, David T.; Linton, Oliver

doi:10.1920/wp.cem.2012.2612

Cited by 2 publications

(1 citation statement)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the proposed estimator is efficient asymptotically, it is not the best estimator with fixed quantiles. Also see Chamberlain (1994), Xiao and Koenker (2009), and Chen, Linton and Jacho-Chavez (2011) for related work on combination of estimators.…”

Section: Introductionmentioning

confidence: 99%

Efficient Regressions via Optimally Combining Quantile Information

Zhao

Zhang

2014

Econom. Theory

View full text Add to dashboard Cite

We develop a generally applicable framework for constructing efficient estimators of regression models via quantile regressions. The proposed method is based on optimally combining information over multiple quantiles and can be applied to a broad range of parametric and nonparametric settings. When combining information over a fixed number of quantiles, we derive an upper bound on the distance between the efficiency of the proposed estimator and the Fisher information. As the number of quantiles increases, this upper bound decreases and the asymptotic variance of the proposed estimator approaches the Cramér-Rao lower bound under appropriate conditions. In the case of non-regular statistical estimation, the proposed estimator leads to super-efficient estimation. We illustrate the proposed method for several widely used regression models. Both asymptotic theory and Monte Carlo experiments show the superior performance over existing methods.

show abstract

Section: Introductionmentioning

confidence: 99%