Constrained Indirect Estimation
提出处理辅助模型参数等式和不等式约束的广义间接估计方法，利用相关乘子提取信息，并与最大似然法比较渐近效率。通过GARCH模型估计随机波动率过程作为示例。
We develop generalized indirect estimation procedures that handle equality and inequality constraints on the auxiliary model parameters by extracting information from the relevant multipliers, and compare their asymptotic efficiency to maximum likelihood. We also show that, regardless of the validity of the restrictions, the asymptotic efficiency of such estimators can never decrease by explicitly considering the multipliers associated with additional equality constraints. Furthermore, we discuss the variety of effects on efficiency that can result from imposing constraints on a previously unrestricted model. As an example, we consider a stochastic volatility process estimated through a garch model with Gaussian or t distributed errors.