非参数动态空间随机前沿模型的贝叶斯估计
Bayesian Estimation of Nonparametric Dynamic Spatial Stochastic Frontier Models
摘要: 空间随机前沿模型忽视了经济变量之间存在的非线性关系，限制了其使用范围。本文构建非参数动态空间随机前沿模型，利用贝叶斯方法估计该模型。数值模拟表明，多元B样条具有良好的拟合能力，模型的估计精度随样本量的增加而提高，无效率项方差的偏差表现不稳定。
Abstract: The spatial stochastic frontier model ignores the nonlinear relationship between economic varia-bles, which limits its scope of use. This article constructs a non parametric dynamic spatial sto-chastic frontier model and uses Bayesian methods to estimate the model. Numerical simulation shows that multivariate B-splines have good fitting ability, and the estimation accuracy of the model increases with the increase of sample size. The deviation of the variance of the invalidity term is unstable.
文章引用：罗林媚. 非参数动态空间随机前沿模型的贝叶斯估计[J]. 理论数学, 2023, 13(4): 1049-1055. https://doi.org/10.12677/PM.2023.134110

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