Estimation Under Inequality Constraints: Semiparametric Estimation of Conditional Duration Models
提出一种半参数估计方法，用于在参数存在不等式约束且误差分布未知时估计条件久期模型，通过约束无约束的半参数有效估计量实现，模拟显示其均方误差优于竞争方法。
This article proposes a semiparametric estimator of the parameter in a conditional duration model when there are inequality constraints on some parameters and the error distribution may be unknown. We propose to estimate the parameter by a constrained version of an unrestricted semiparametrically efficient estimator. The main requirement for applying this method is that the initial unrestricted estimator converges in distribution. Apart from this, additional regularity conditions on the data generating process or the likelihood function, are not required. Hence the method is applicable to a broad range of models where the parameter space is constrained by inequality constraints, such as the conditional duration models. In a simulation study involving conditional duration models, the overall performance of the constrained estimator was better than its competitors, in terms of mean squared error. A data example is used to illustrate the method.