Efficiency spillovers in Bayesian stochastic frontier models: application to electricity distribution in New Zealand

Carvalho, Antonio

doi:10.1080/17421772.2018.1444280

Cited by 17 publications

(12 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These considerations suggest the use of alternative inferential procedures which sidestep the direct evaluation of the n-variate integrals. Schmidt et al (2009), Areal et al (2012), Tsionas & Michaelides (2016) and Carvalho (2018) exploit the Bayesian paradigm, thus estimating the inefficiencies alongside the other model parameters through simulation. Schmidt et al (2009) propose a specification for panel data in which the inefficiencies are modeled using a truncated normal distribution in which the pre-truncated means are heterogeneous and spatially correlated via a conditional autoregressive prior.…”

Section: Literature Reviewmentioning

confidence: 99%

“…9 Finally, this specification does not allow to have efficiency spillovers among the units, since the correlation between the means does not imply a direct effect of the inefficiency of unit i on the one of the neighboring unit j. Areal et al (2012), Tsionas & Michaelides (2016) and Carvalho (2018) consider special cases of our statistical model (e.g., time invariant and/or homoskedastic inefficiencies). However, the data augmentation step for the intrinsic inefficiencies proposed by Areal et al (2012) and Carvalho (2018) assumes that the conditional distribution f (ũ|y, θ), where θ is the vector of unknown parameters to be estimated, can be factorized as independent univariate distributions while, even if the intrinsic inefficiencies are a priori independent, their conditional distribution is still multivariate.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency

Belotti

Ilardi

Mortari³

2019

SSRN Journal

View full text Add to dashboard Cite

This paper proposes a stochastic frontier panel data model in which unit-specific inefficiencies are spatially correlated. In particular, this model has simultaneously three important features: i) the total inefficiency of a productive unit depends on its own inefficiency and on the inefficiency of its neighbors; ii) the spatially correlated and time varying inefficiency is disentangled from time invariant unobserved heterogeneity in a panel data modelà la Greene (2005); iii) systematic differences in inefficiency can be explained using exogenous determinants. We propose to estimate both the "true" fixed-and random-effects variants of the model using a feasible simulated composite maximum likelihood approach. The finite sample behavior of the proposed estimators are investigated through a set of Monte Carlo experiments. Our simulation results suggest that the estimation approach is consistent, showing good finite sample properties especially in small samples.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency

Belotti

Ilardi

Mortari³

2019

SSRN Journal

View full text Add to dashboard Cite

show abstract

“…Second, the papers also offer interesting contrasts on alternative ways of modelling spatial dependence. While Breitung and Wigger (2017) and Carvalho (2018) focus on the spatial error model and Ditzen (2017) on the spatial Durbin model (SDM), Doğan et al (2017) and Wang and Lee (2017) consider combinations of spatial lag and spatial error models. Further, Ditzen (2017) and Wang and Lee (2017) highlight alternate ways of modelling spatial dependence arising from common factors.…”

mentioning

confidence: 99%

“…Third, peer and social networks constitute one important application area of spatial econometrics (Bhattacharjee & Holly, 2013;Liu, Patacchini, & Zenou, 2014), which has received relatively little coverage in Spatial Economic Analysis and allied journals. This issue contains two papers: Carvalho (2018) and Doğan et al (2017), that are directly related to peer networks, while another two, Breitung and Wigger (2017) and Wang and Lee (2017), also have connections to this topic. We hope this collection will contribute towards more active research on social networks in the spatial econometrics tradition.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Contemporary developments in the theory and practice of spatial econometrics

Bhattacharjee

Holly

Mur

2018

Spatial Economic Analysis

View full text Add to dashboard Cite

The papers in this special issue cover a wide range of areas in the methodology and application of spatial econometrics. The first develops a generalized method of moments (GMM) estimator for the spatial regression model from a second-order approximation to the maximum likelihood (ML). The second develops Bayesian estimation in a stochastic frontier model with network dependence in efficiencies, with application to industry dynamics. The third studies crosscountry convergence under the Lotka-Volterra model and obtains new insights into spatial spillovers. The penultimate paper develops robust specification tests for the social interactions model under both ML and GMM frameworks. The final paper proposes identification and GMM estimation in a high-order spatial autoregressive model with heterogeneity, common factors and spatial error dependence. KEYWORDS spatial econometrics, panel data, social networks, generalized method of moments (GMM), Bayesian methods, Lotka-Volterra model JEL C11, C21, C23, C38, C52 This special issue collects selected papers from the 26th (EC) 2 Conference on the 'Theory and Practice of Spatial Econometrics', organized in December 2015 by the Spatial Economics & Econometrics Centre (SEEC), Heriot-Watt University, Edinburgh, UK. 1 The conference was a great success, with wide participation and high quality of papers. Subsequently, Spatial Economic Analysis, one of the leading field journals in the area, approached the organizers with an invitation to organize a special issue. Unfortunately, the preparation of this special issue also happened in the shadow of the loss of two of the leading researchers in the areas of spatial econometrics and regional science: Cem Ertur (1962-2016) and Raymond J. G. M. Florax (1956-2017). Beyond their important roles as leading researchers in the area, they were also dear friends and colleagues to many of us. Though neither Cem nor Raymond was able to attend the conference in person, they both had significant presence intellectually: in the papers presented at the conference, in the literatures that the presented papers contributed to and, likewise, in the papers in this issue. The papers submitted to this special issue were subjected to the regular review standards and processes of Spatial Economic Analysis. The five selected papers are, individually and collectively,

show abstract

Spatial dependence in production frontier models

Ayouba

2023

J Prod Anal

View full text Add to dashboard Cite

Efficiency spillovers in Bayesian stochastic frontier models: application to electricity distribution in New Zealand

Cited by 17 publications

References 37 publications

Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency

Estimation of Stochastic Frontier Panel Data Models with Spatial Inefficiency

Contemporary developments in the theory and practice of spatial econometrics

Spatial dependence in production frontier models

Contact Info

Product

Resources

About