Estimation of a dynamic stochastic frontier model using likelihood‐based approaches
提出一个允许技术无效率动态调整的面板随机生产前沿模型，假设无效率服从AR(1)过程，并用四种似然方法估计模型，通过蒙特卡洛模拟和芬兰电力公司数据验证其性能。
Summary This paper considers a panel stochastic production frontier model that allows the dynamic adjustment of technical inefficiency. In particular, we assume that inefficiency follows an AR(1) process. That is, the current year's inefficiency for a firm depends on its past inefficiency plus a transient inefficiency incurred in the current year. Interfirm variations in the transient inefficiency are explained by some firm‐specific covariates. We consider four likelihood‐based approaches to estimate the model: the full maximum likelihood, pairwise composite likelihood, marginal composite likelihood, and quasi‐maximum likelihood approaches. Moreover, we provide Monte Carlo simulation results to examine and compare the finite‐sample performances of the four above‐mentioned likelihood‐based estimators of the parameters. Finally, we provide an empirical application of a panel of 73 Finnish electricity distribution companies observed during 2008–2014 to illustrate the working of our proposed models.