Abstract We consider a stochastic frontier regression model with a time dependent efficiency process, which is assumed to follow an exponential autoregressive sequence. The likelihood for the model is derived in the context of a bivariate exponential distribution. Bayesian method is suggested for the estimation of parameters. We apply the model and the estimation procedure to a panel of US airlines data and show empirically that the model is dynamic in the sense that it reveals improvement in the efficiency or reduction in the inefficiency over time.

中文翻译：

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具有动态前沿的随机前沿回归模型

摘要 我们考虑具有时间相关效率过程的随机前沿回归模型，该模型假定遵循指数自回归序列。该模型的似然是在二元指数分布的上下文中导出的。建议使用贝叶斯方法来估计参数。我们将模型和估计程序应用于一组美国航空公司数据，并凭经验表明该模型是动态的，因为它揭示了效率随时间的提高或低效率的减少。