基于动态随机前沿模型的集装箱码头技术效率分析

doi:10.12066/j.issn.1007-2861.1840

上海大学学报(自然科学版) ›› 2018, Vol. 24 ›› Issue (4): 609-616.doi: 10.12066/j.issn.1007-2861.1840

基于动态随机前沿模型的集装箱码头技术效率分析

张世斌¹^,²(), 时寰²

1. 上海海事大学数学系, 上海 201306
2. 上海海事大学物流研究中心, 上海 201306

收稿日期:2016-09-08 出版日期:2018-08-31 发布日期:2018-08-31
通讯作者: 张世斌 E-mail:sbzhang@shmtu.edu.cn
基金资助:
国家自然科学基金资助项目(11671416);全国统计科学研究资助项目(2015LY33)

Analysis of technical efficiency of container terminals based on a dynamic stochastic frontier model

ZHANG Shibin¹^,²(), SHI Huan²

1. Department of Mathematics, Shanghai Maritime University, Shanghai 201306, China
2. Logistics Research Center, Shanghai Maritime University, Shanghai 201306, China

Received:2016-09-08 Online:2018-08-31 Published:2018-08-31
Contact: ZHANG Shibin E-mail:sbzhang@shmtu.edu.cn

摘要/Abstract

摘要：

基于动态随机前沿模型, 利用贝叶斯统计方法, 通过 2007---2012 年长三角地区 14 个主要集装箱码头的面板数据对技术效率及其影响因素进行动态分析. 研究集装箱码头设备、建港条件等对其产出的影响, 以及集疏运体系、人力资源状况等对其技术效率的影响. 研究发现: 码头装卸设备对其产出的提升作用高于集装箱堆场面积和泊位长度等要素; 水水中转比例、劳务工比例均对提升码头效率有正向作用; 国际中转比例及员工教育水平对码头效率影响甚微.

关键词: 动态随机前沿模型, 贝叶斯分析, 集装箱港口, 技术效率

Abstract:

Based on a dynamic stochastic frontier model and the Bayesian statistical method, the technical efficiency and its effect factors of the ports are analyzed by aid of the 2007---2012 panel data of 14 container terminals in the Yangtze Delta. In the modeling, handling equipment and regional factors are introduced as covariates to analyze their effects on the output of the terminal. The transportation system of the port and the employee composition are introduced as covariates to study their effects on the technical efficiency. It is evidenced that the output of the port benefits more from the inputs of handling equipment than from the area of yards and the length of berths. For exogenous variables, improvement in technical efficiency benefits more from the higher ratio of water transshipment and the larger number of worker than from the longer period of employee education and the higher ratio of international transshipment business.

Key words: dynamic stochastic frontier model, Bayesian analysis, container terminal, technical efficiency

中图分类号:

张世斌, 时寰. 基于动态随机前沿模型的集装箱码头技术效率分析[J]. 上海大学学报(自然科学版), 2018, 24(4): 609-616.

ZHANG Shibin, SHI Huan. Analysis of technical efficiency of container terminals based on a dynamic stochastic frontier model[J]. Journal of Shanghai University（Natural Science Edition）, 2018, 24(4): 609-616.

图/表 4

参考文献 18

[1]	Yan J, Sun X, Liu J J . Assessing container operator efficiency with heterogeneous and time-varying production frontiers[J]. Transportation Research Part B: Methodological, 2009,43(1):172-185.
[2]	Aigner D, Lovell C, Schmidt P . Formulation and estimation of stochastic frontier production function models[J]. Journal of Econometrics, 1977,6(1):21-37.
[3]	Meeusen W, Van Den Broeck J . Efficiency estimation from Cobb-Douglas production functions with composed error[J]. International Economic Review, 1977,18(2):435-444.
[4]	Chen Z F, Barros C, Borges M . A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies[J]. Energy Economics, 2015,48(C):136-144.
[5]	Tabak B M, Tecles P L . Estimating a Bayesian stochastic frontier for the Indian banking system[J]. International Journal of Production Economics, 2010,125(1):96-110. doi: 10.1016/j.ijpe.2010.01.008
[6]	Cullinane K, Song D W . A stochastic frontier model of the productive efficiency of Korean container terminals[J]. Applied Economics, 2003,35(3):251-267.
[7]	艾亚钊, 周晓坤 . 基于随机前沿分析(SFA)法的集装箱港口效率分析[J]. 上海海事大学学报, 2015,36(2):31-35.
[8]	Yip T L, Sun X Y, Liu J J . Group and individual heterogeneity in a stochastic frontier model: container terminal operators[J]. European Journal of Operational Research, 2011,213(3):517-525.
[9]	陈春芳, 宗蓓华 . 基于 SFA 的上海港集装箱码头效率评价[J]. 上海海事大学学报, 2008,29(3):87-92.
[10]	邓蕾, 唐沙沙 . 中国集装箱港口企业技术效率影响要素研究[J]. 重庆大学学报 (社会科学版), 2014,20(1):75-79.
[11]	Tsionas E G . Inference in dynamic stochastic frontier models[J]. Journal of Applied Econometrics, 2006,21(5):669-676.
[12]	Zhang Q, Xu Z Z, Feng T J , et al. A dynamic stochastic frontier model to evaluate regional financial efficiency: evidence from Chinese county-level panel data[J]. European Journal of Operational Research, 2015,241(3):907-916.
[13]	罗芳, 赵景景 . 长江三角洲主要港口的功能定位分析[J]. 工业技术经济, 2012(4):15-21.
[14]	王桂英 . 发展国际集装箱水水中转助推上海国际航运中心建设[J]. 物流工程与管理, 2012,34(5):1-3.
[15]	刘晓君, 张世斌 . 正态倒Gamma 随机前沿模型的 Bayesian 推断[J]. 高校应用数学学报, 2013,28(4):488-496.
[16]	程迪, 张世斌 . 动态异方差随机前沿模型的 Bayesian 推断[J]. 高校应用数学学报, 2016,31(2):127-135.
[17]	Robert C P, Casella G . Monte Carlo statistical methods[M]. 2nd ed. New York: Springer-Verlag, 2005.
[18]	肖忠熙 . 集装箱国际中转比重大并非国际航运中心的必备条件[J]. 水运管理, 2006,28(12):6-9.

基于动态随机前沿模型的集装箱码头技术效率分析

Analysis of technical efficiency of container terminals based on a dynamic stochastic frontier model

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