TY  - JOUR
A1  - Neumann, Anne
A1  - Nieswand, Maria
A1  - Schubert, Torben
T1  - Estimating alternative technology sets in nonparametric efficiency analysis: restriction tests for panel and clustered data
JF  - Journal of productivity analysis
N2  - Nonparametric efficiency analysis has become a widely applied technique to support industrial bench-marking as well as a variety of incentive-based regulation policies. In practice such exercises are often plagued by incomplete knowledge about the correct specifications of inputs and outputs. Simar and Wilson (Commun Stat Simul Comput 30(1): 159-184, 2001) and Schubert and Simar (J Prod Anal 36(1): 55-69, 2011) propose restriction tests to support such specification decisions for cross-section data. However, the typical oligopolized market structure pertinent to regulation contexts often leads to low numbers of cross-section observations, rendering reliable estimation based on these tests practically unfeasible. This small-sample problem could often be avoided with the use of panel data, which would in any case require an extension of the cross-section restriction tests to handle panel data. In this paper we derive these tests. We prove the consistency of the proposed method and apply it to a sample of US natural gas transmission companies from 2003 through 2007. We find that the total quantity of natural gas delivered and natural gas delivered in peak periods measure essentially the same output. Therefore only one needs to be included. We also show that the length of mains as a measure of transportation service is non-redundant and therefore must be included.
KW  - Benchmarking models
KW  - Network industries
KW  - Nonparametric efficiency estimation
KW  - Data envelopment analysis
KW  - Testing restrictions
KW  - Subsampling
KW  - Bootstrap
Y1  - 2016
U6  - https://doi.org/10.1007/s11123-015-0461-z
SN  - 0895-562X
SN  - 1573-0441
VL  - 45
SP  - 35
EP  - 51
PB  - Springer
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Beinrucker, Andre
A1  - Dogan, Urun
A1  - Blanchard, Gilles
T1  - Extensions of stability selection using subsamples of observations and covariates
JF  - Statistics and Computing
N2  - We introduce extensions of stability selection, a method to stabilise variable selection methods introduced by Meinshausen and Buhlmann (J R Stat Soc 72:417-473, 2010). We propose to apply a base selection method repeatedly to random subsamples of observations and subsets of covariates under scrutiny, and to select covariates based on their selection frequency. We analyse the effects and benefits of these extensions. Our analysis generalizes the theoretical results of Meinshausen and Buhlmann (J R Stat Soc 72:417-473, 2010) from the case of half-samples to subsamples of arbitrary size. We study, in a theoretical manner, the effect of taking random covariate subsets using a simplified score model. Finally we validate these extensions on numerical experiments on both synthetic and real datasets, and compare the obtained results in detail to the original stability selection method.
KW  - Variable selection
KW  - Stability selection
KW  - Subsampling
Y1  - 2016
U6  - https://doi.org/10.1007/s11222-015-9589-y
SN  - 0960-3174
SN  - 1573-1375
VL  - 26
SP  - 1059
EP  - 1077
PB  - Springer
CY  - Dordrecht
ER  -