A spatial stochastic frontier model including both frontier and error-based spatial cross-sectional dependence

REFERENCES

• Adetutu, M., Glass, A. J., Kenjegalieva, K., & Sickles, R. C. (2015). The effects of efficiency and TFP growth on pollution in Europe: A multistage spatial analysis. Journal of Productivity Analysis, 43(3), 307–326. doi:10.1007/s11123-014-0426-7
   Google Scholar
• Areal, F. J., Balcombe, K., & Tiffin, R. (2012). Integrating spatial dependence into stochastic frontier analysis. Australian Journal of Agricultural and Resource Economics, 56(4), 521–541. doi:10.1111/j.1467-8489.2012.00597.x
   Google Scholar
• Banca d’Italia. (2019). Economie Regionali. L’economia della Calabria. https://www.bancaditalia.it/pubblicazioni/economie-regionali/2019/2019-0018/1918-calabria.pdf
   Google Scholar
• Baptista, R. (2000). Do innovations diffuse faster within geographical clusters? International Journal of Industrial Organization, 18(3), 515–535. doi:10.1016/S0167-7187(99)00045-4
   Google Scholar
• Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332. doi:10.1007/BF01205442
   Google Scholar
• Beebe, C., Haque, F., Jarvis, C., Kenney, M., & Patton, D. (2013). Identity creation and cluster construction: The case of the Paso robles wine region. Journal of Economic Geography, 13(5), 711–740. doi:10.1093/jeg/lbs033
   Google Scholar
• Billé, A. G., Salvioni, C., & Benedetti, R. (2018). Modelling spatial regimes in farms technologies. Journal of Productivity Analysis, 49(2), 173–185. doi:10.1007/s11123-018-0529-7
   Google Scholar
• Cardamone, P. (2020). Productivity and spatial proximity: Evidence from the Italian food industry. International Review of Applied Economics, 34(3), 327–341. doi:10.1080/02692171.2020.1732308
   Google Scholar
• Chen, Z., Huffman, W., & Rozelle, S. (2009). Farm technology and technical efficiency: Evidence from four regions in China. China Economic Review, 20(2), 153–161. doi:10.1016/j.chieco.2009.03.002
   Google Scholar
• Elhorst, J. P. (2010). Spatial panel data models. In M. M. Fischer & A. Getis (Eds.), Handbook of applied spatial analysis. Springer.
   Google Scholar
• Fusco, E., & Vidoli, F. (2013). Spatial stochastic frontier models: Controlling spatial global and local heterogeneity. International Review of Applied Economics, 27(5), 679–694. doi:10.1080/02692171.2013.804493
   Google Scholar
• Glass, A. J., Kenjegalieva, K., & Sickles, R. C. (2016). A spatial autoregressive stochastic frontier model for panel data with asymmetric efficiency spillovers. Journal of Econometrics, 190(2), 289–300. doi:10.1016/j.jeconom.2015.06.011
   Google Scholar
• Griliches, Z. (1992). The search for R&D spillovers. The Scandinavian Journal of Economics, 94, S29–S47. doi:10.2307/3440244
   Google Scholar
• Gude, A., Alvarez, I., & Orea, L. (2018). Heterogeneous spillovers among Spanish provinces: A generalized spatial stochastic frontier model. Journal of Productivity Analysis, 50(3), 155–173. doi:10.1007/s11123-018-0540-z
   Google Scholar
• Kelejian, H. H., & Prucha, I. R. (1998). A generalized spatial two stage least squares procedure for estimating a spatial autoregressive model with autoregressive disturbance. The Journal of Real Estate Finance and Economics, 17(1), 99–121. doi:10.1023/A:1007707430416
   Google Scholar
• Kelejian, H. H., & Prucha, I. R. (1999). A generalized moments estimator for the autoregressive parameter in a spatial model. International Economic Review, 40(2), 509–533. doi:10.1111/1468-2354.00027
   Google Scholar
• Latruffe, L. (2010). Competitiveness, productivity and efficiency in the agricultural and agri-food sectors. In OECD Food, Agriculture and Fisheries Working Papers, No. 30. Paris, France: OECD Publishing.
   Google Scholar
• LeSage, J. P., & Pace, R. K. (2009). Introduction to spatial econometrics. Taylor & Francis.
   Google Scholar
• Marshall, A. (1890). Principles of economics. Macmillan.
   Google Scholar
• Mathijs, E., & Vranken, L. (2001). Human capital, gender and organisation in transition agriculture: Measuring and explaining the technical efficiency of Bulgarian and Hungarian farms. Post-Communist Economies, 13(2), 171–187. doi:10.1080/14631370120052654
   Google Scholar
• Mizruchi, M. S., & Neuman, E. J. (2008). The effect of density on the level of bias in the network autocorrelation model. Social Networks, 30(3), 190–200. doi:10.1016/j.socnet.2008.02.002
   Google Scholar
• Orea, L., & Alvarez, I. C. (2019). A new stochastic frontier model with cross-sectional effects in both noise and inefficiency terms. Journal of Econometrics, 213(2), 556–577. doi:10.1016/j.jeconom.2019.07.004
   Google Scholar
• Ramajo, J., & Hewings, G. J. (2018). Modelling regional productivity performance across Western Europe. Regional Studies, 52(10), 1372–1387. doi:10.1080/00343404.2017.1390219
   Google Scholar
• Rizov, M., Pokrivcak, J., & Ciaian, P. (2013). CAP subsidies and productivity of the EU farms. Journal of Agricultural Economics, 64(3), 537–557. doi:10.1111/1477-9552.12030
   Google Scholar
• Schmidt, A. M., Moreira, A. R. B., Helfand, S. M., & Fonseca, T. C. O. (2009). Spatial stochastic frontier models: Accounting for unobserved local determinants of inefficiency. Journal of Productivity Analysis, 31(2), 101–112. doi:10.1007/s11123-008-0122-6
   Google Scholar
• Skevas, I., & Lansink, A. O. (2020). Dynamic inefficiency and spatial spillovers in Dutch dairy farming. Journal of Agricultural Economics, 71(3), 742–759. doi:10.1111/1477-9552.12369
   Google Scholar
• Stetter, C., & Sauer, J. (2021). Exploring the heterogeneous effects of weather on productivity using generalized random forests. 95th Annual Conference, March 29-30, 2021, Warwick, UK (Hybrid) 312074, Agricultural Economics Society - AES.
   Google Scholar
• Tsionas, E. G., & Michaelides, P. G. (2016). A spatial stochastic frontier model with spillovers: Evidence for Italian regions. Scottish Journal of Political Economy, 63(3), 243–257. doi:10.1111/sjpe.12081
   Google Scholar
• Tsukamoto, T. (2019). A spatial autoregressive stochastic frontier model for panel data incorporating a model of technical inefficiency. Japan and the World Economy, 50, 66–77. doi:10.1016/j.japwor.2018.11.003
   Google Scholar
• Vidoli, F., Cardillo, C., Fusco, E., & Canello, J. (2016). Spatial nonstationarity in the stochastic frontier model: An application to the Italian wine industry. Regional Science and Urban Economics, 61, 153–164. doi:10.1016/j.regsciurbeco.2016.10.003
   Google Scholar
• Wang, H. J., & Ho, C. W. (2010). Estimating fixed-effect panel stochastic frontier models by model transformation. Journal of Econometrics, 157(2), 286–296. doi:10.1016/j.jeconom.2009.12.006
   Google Scholar
• Wang, H. J., & Schmidt, P. (2002). One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels. Journal of Productivity Analysis, 18(2), 129–144. doi:10.1023/A:1016565719882
   Google Scholar
• Yao, S., & Liu, Z. (1998). Determinants of grain production and technical efficiency in China. Journal of Agricultural Economics, 49(2), 171–184. doi:10.1111/j.1477-9552.1998.tb01262.x
   Google Scholar