Jeffrey, Stephen Glenn (2012) Quantile regression and frontier analysis. PhD thesis, University of Warwick.

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Abstract

In chapter 3, quantile regression is used to estimate probabilistic frontiers,
i.e. frontiers based on the probability of being dominated. The
results from the empirical application using an Italian hotel dataset
show rejections of a parametric functional form and a location shift
effect, large uncertainty of the estimates of the frontier and wide confidence
intervals for the estimates of efficiency. Quantile regression is
further developed to estimate thick probabilistic frontiers, i.e. frontiers
based on a group of efficient firms. The empirical results show
that the differences between the inefficient and efficient firms at lower
quantiles of the conditional distribution function are from the coefficient
(85 percent of the total effect) and the residual effects (25
percent) and at higher quantiles from the coefficient (68 percent) and
the regressor effects (22 percent).
The results from the Monte Carlo simulations in chapter 4 show that
under the correctly assumed stochastic frontier models, the probabilistic
frontiers can have the lowest bias and mean squared error of the
efficiency estimates. When outliers or location-scale shift effects are
included, more preference is towards the probabilistic frontiers. The
nonparametric probabilistic frontiers are nearly always preferable to
Data Envelopment Analysis and Free Disposable Hull.
In chapter 5, a fixed effects quantile regression estimator is used to
estimate a cost frontier and efficiency levels for a panel dataset of
English NHS Trusts. Waiting times elasticities are estimated from
-0.14 to 0.17 in the cross-sectional models and -0.008 to 0.03 in the
panel models. Cost minimisation ranged from 33 to 60 days in the
cross-sectional model and from 37 to 54 days in the panel model. The
results show that the effects of the inputs and control variables vary
depending on the efficiency of the Trusts. The efficiency estimates
reveal very different conclusions depending on the model choice.

Item Type:	Thesis or Dissertation (PhD)
Subjects:	H Social Sciences > HB Economic Theory Q Science > QA Mathematics
Library of Congress Subject Headings (LCSH):	Regression analysis, Econometric models
Official Date:	January 2012
Dates:	Date Event January 2012 Submitted
Institution:	University of Warwick
Theses Department:	Department of Economics
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Waterson, Michael, 1950- ; Pitt, Michael
Sponsors:	Economic and Social Research Council (Great Britain) (ESRC)
Extent:	x, 147 leaves : charts
Language:	eng

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