A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms

Abstract

This paper develops a new stochastic frontier model that allows for cross-sectional (spatial) correlation in both the noise and inefficiency terms. The model proposed is useful in efficiency analyses when there are omitted but spatially-correlated variables and firms benefit from best practices implemented by other (adjacent) firms. Unlike the previous literature, our model can be estimated by maximum likelihood using standard software. The model is illustrated with an application to the Norwegian electricity distribution sector.

Estimating the propagation of both reported and undocumented COVID-19 cases in Spain: a panel data frontier approximation of epidemiological models

“…We now turn to the part of the likelihood function related to the proportion of undocumented cases. Estimating (11) is far from straightforward because the distribution of ∆ u it is generally not known if we assume that u it is independently distributed across provinces and over time (see, for instance, Wang 2003 , and Orea and Álvarez 2019 ). To deal with this issue, we follow Wang and Ho ( 2010 ) and assume that u it possesses the so-called scaling property so that it can be multiplicatively decomposed into two components as follows: where h it = h ( z it , τ ) ≥ 0 is a deterministic (scaling) function, z it is a set of undocumented-cases determinants (often labelled as contextual or z -variables), and u i is a homoskedastic one-sided random variable.…”

Dynamic returns to scale and geography in U.S. banking

“…Studies such as those considered above that include the distance between the locations of a bank's activities as a determinant report estimates of the own distance effect. We build on this approach in the following three respects by drawing on the growing number of studies that use methods from spatial data science to analyze returns to scale (Glass, Kenjegaliev, & Kenjegalieva, 2020); technical efficiency (e.g., Algeri et al, 2022; Glass, Kenjegalieva, & Weyman‐Jones, 2020; Horrace et al, 2019; Orea & Álvarez, 2019; Tsionas & Michaelides, 2016); and productivity (Glass et al, 2013; Glass & Kenjegalieva, 2019; Glass, Kenjegalieva, & Douch, 2020). All these studies have a static spatial framework and hence focus exclusively on contemporaneous spatial relationships.…”

Spatial dependence in the technical efficiency of local banks

“…To bolster our empirical findings on spatial spillover effects in the TE of small cooperative banks, we apply the alternative SFA technique, which is based on nonparametric estimation. There is a body of literature on SFA that allows for the assessment of TE in the presence of geographical spillovers (Glass et al, 2014; 2016; Kutlu, 2018; Kutlu et al, 2020; Orea & Álvarez, 2019).…”