Publication Type

Working Paper

Publication Date

9-2017

Abstract

This article proposes two novel estimation and inference approaches for production frontiers based on extreme quantiles of feasible outputs. The first approach linearly combines two extreme quantiles to reduce the estimation bias, and uses a subsampling method to construct point estimates and confidence intervals. The second approach can accommodate any finite number of extreme quantile estimates by way of the Approximate Bayesian Computation method. The point estimators and confidence intervals are then obtained through the Markov Chain Monte Carlo algorithm. The estimations and inferences of both approaches are justified asymptotically. Their finite sample performances are illustrated through simulations and an empirical application.

Keywords

Fixed-k asymptotics, extreme value theory

Discipline

Econometrics

Research Areas

Econometrics

First Page

1

Last Page

59

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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Econometrics Commons

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