Testing tail monotonicity by constrained copula estimation

Gijbels, Irène; Sznajder, Dominik

doi:10.1016/j.insmatheco.2013.01.006

Cited by 7 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The conditional expected maximum production function enjoys the property of monotonicity if and only if the nonstandard conditional distribution function of Y given X ≤ x is nonincreasing in x (see Thm. A.3 in Cazals et al, 2002); this necessary and sufficient condition is referred to as tail monotonicity (see, e.g., Gijbels and Sznajder, 2013). Second and most importantly, even if the theoretical hypothesis of tail monotonicity is satisfied, the empirical estimators of the conditional expected maximum production function, needed to be used in practice, are not constrained to enjoy the property of monotonicity.…”

Section: Introductionmentioning

confidence: 99%

Robustified Expected Maximum Production Frontiers

2020

View full text Add to dashboard Cite

The aim of this paper is to construct a robust nonparametric estimator for the production frontier. We study this problem under a regression model with one-sided errors, where the regression function defines the achievable maximum output, for a given level of inputs-usage, and the regression error defines the inefficiency term. The main tool is a concept of partial regression boundary defined as a special probability-weighted moment. This concept motivates a robustified unconditional alternative to the pioneering class of nonparametric conditional expected maximum production functions. We prove that both the resulting benchmark partial frontier and its estimator share the desirable monotonicity of the true full frontier. We derive the asymptotic properties of the partial and full frontier estimators, and unravel their behavior from a robustness theory point of view. We provide numerical illustrations and Monte Carlo evidence that the presented concept of unconditional expected maximum production functions is more efficient and reliable in filtering out noise than the original conditional version. The methodology is very easy and fast to implement. Its usefulness is discussed through two concrete datasets from the sector of Delivery Services, where outliers are likely to affect the traditional conditional approach.

show abstract

Section: Introductionmentioning

confidence: 99%