Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales
提出一种贝叶斯方法，通过地理加性预测变量在随机前沿模型的低效率项中建模区域依赖结构和空间异质性，发现英格兰和威尔士谷物生产存在区域低效率模式，忽略区域共同表现会导致估计偏差。
We propose a flexible Bayesian approach to inefficiency modelling that accounts for regional patterns of local performance. The model allows for a separated treatment of individual heterogeneity and determinants of inefficiency. Regional dependence structures and location-specific unobserved spatial heterogeneity are modelled via geoadditive predictors in the inefficiency term of the stochastic frontier model. Inference becomes feasible through Markov chain Monte Carlo simulation techniques. In an empirical illustration we find that regional patterns of inefficiency characterize cereal production in England and Wales. Neglecting common performance patterns of farms located in the same region induces systematic biases to inefficiency estimates.