Elisa Fusco, Giuseppe Arbia, Francesco Vidoli, Vincenzo Nardelli
arXiv 28 Oct 2024 · Statistics — Methodology · 1 citations (OpenAlex)
arXiv:2410.20915 · PDF · DOI · OpenAlex · Extracted main text
In the literature on stochastic frontier models until the early 2000s, the joint consideration of spatial and temporal dimensions was often inadequately addressed, if not completely neglected. However, from an evolutionary economics perspective, the production process of the decision-making units constantly changes over both dimensions: it is not stable over time due to managerial enhancements and/or internal or external shocks, and is influenced by the nearest territorial neighbours. This paper proposes an extension of the Fusco and Vidoli [2013] SEM-like approach, which globally accounts for spatial and temporal effects in the term of inefficiency. In particular, coherently with the stochastic panel frontier literature, two different versions of the model are proposed: the time-invariant and the time-varying spatial stochastic frontier models. In order to evaluate the inferential properties of the proposed estimators, we first run Monte Carlo experiments and we then present the results of an application to a set of commonly referenced data, demonstrating robustness and stability of estimates across all scenarios.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Elisa Fusco and Francesco Vidoli (2013) Spatial stochastic frontier models: controlling spatial global and local heterogeneity self | 0.928 | 4 | 3 | 100% |
| 2 | Viliam Druska and William C. Horrace (2004) Generalized moments estimation for spatial panel data: Indonesian rice farming | 0.874 | 7 | 2 | 100% |
| 3 | Elisa Fusco Spatial dependence in efficiency parametric models: a generalization and simulation studies self | 0.693 | 6 | 1 | 100% |
| 4 | D. Aigner, C. A. K. Lovell, and P. Schmidt (1977) Formulation and estimation of stochastic frontier production function models | 0.644 | 2 | 2 | 100% |
| 5 | G. E. Battese and T. J. Coelli (1992) Frontier production functions, technical efficiency and panel data: With application to paddy farmers in india | 0.585 | 3 | 1 | 100% |
| 6 | E Fusco and F Vidoli (2023) ssfa: Spatial Stochastic Frontier Analysis, 2023 | 0.511 | 2 | 2 | 50% |
| 7 | Francisco José Areal, Kelvin Balcombe, and Richard Tiffin (2012) Integrating spatial dependence into stochastic frontier analysis | 0.511 | 2 | 1 | 100% |
| 8 | A.J. Glass, K. Kenjegalieva, and R.C. Sickles (2016) A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers | 0.511 | 2 | 1 | 100% |
| 9 | Ron A Boschma and Jan G Lambooy (1999) Evolutionary economics and economic geography | 0.511 | 2 | 1 | 100% |
| 10 | Efthymios G Tsionas and Panayotis G Michaelides (2016) A spatial stochastic frontier model with spillovers: Evidence for italian regions | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 42 scored citations.