Rights statement: This is the peer reviewed version of the following article: Tsionas, E. G. and Michaelides, P. G. (2016), A Spatial Stochastic Frontier Model with Spillovers: Evidence for Italian Regions. Scottish Journal of Political Economy. doi: 10.1111/sjpe.12081 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/sjpe.12081/abstract . This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A spatial stochastic frontier model with spillovers
T2 - evidence for Italian regions
AU - Tsionas, Efthymios
AU - Michaelides, Panayotis G.
PY - 2016/7
Y1 - 2016/7
N2 - Efficiency measurement using stochastic frontier models is well established in applied econometrics. However, no published work seems to be available on efficiency analysis using spatial data dealing with possible spatial dependence between regions. This article considers a stochastic frontier model with decomposition of inefficiency into an idiosyncratic and a spatial, spillover component. Exact posterior distributions of parameters are derived, and computational schemes based on Gibbs sampling with data augmentation are proposed to conduct simulation-based inference and efficiency measurement. The new method is illustrated using production data for Italian regions (1970–1993). Clearly, further theoretical and empirical research on the subject would be of great interest.
AB - Efficiency measurement using stochastic frontier models is well established in applied econometrics. However, no published work seems to be available on efficiency analysis using spatial data dealing with possible spatial dependence between regions. This article considers a stochastic frontier model with decomposition of inefficiency into an idiosyncratic and a spatial, spillover component. Exact posterior distributions of parameters are derived, and computational schemes based on Gibbs sampling with data augmentation are proposed to conduct simulation-based inference and efficiency measurement. The new method is illustrated using production data for Italian regions (1970–1993). Clearly, further theoretical and empirical research on the subject would be of great interest.
KW - Spatial econometrics
KW - Stochastic frontier
KW - econometrics
U2 - 10.1111/sjpe.12081
DO - 10.1111/sjpe.12081
M3 - Journal article
VL - 63
SP - 243
EP - 257
JO - Scottish Journal of Political Economy
JF - Scottish Journal of Political Economy
SN - 0036-9292
IS - 3
ER -