Analytic Derivatives for Estimation of Discrete-Time, Linear-Quadratic, Dynamic, Optimization Models
离散时间线性二次动态优化模型估计的解析导数AI 解读 ↓
本页整理公开书目信息,不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成,未完成全文核验,可能有误;具体假设、证明与结论请核对原文。摘要保留来源文本,未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要;缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录,不合计多个记录;关联记录可能包含正式发表版本,不等于此版本单独引用。缺失不视为零,引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配,同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列,再参考通用方法与发表日期。
AI 根据所提供的论文材料归类:研究以动态优化模型的参数及最优政策矩阵为对象,直接服务于结构模型的参数估计。
离散时间线性二次动态优化模型估计的解析导数AI 解读 ↓
正在核对关联版本…
正在核对对应版本的资源…
ESTIMATION OF PARAMETERS in discrete-time, linear-quadratic, infinite-horizon, dynamic, optimization models (Hansen and Sargent, 1980, 1981) is a nonlinear estimation problem. An impediment to effectively computing parameter estimates and their covariances in these models with gradient algorithms (Kennedy and Gentle, 1980, pp. 425-512) has been the absence of expressions for computing analytic VoP, the gradient matrix of first-partial derivatives of the optimal policy matrix P with respect to parameters of the optimization problem collected in vector 0. Derivatives can always be approximated numerically, but gradient algorithms perform more reliably, accurately, and quickly when analytic derivatives are used. In this paper we derive linear systems whose solution yields analytic V P. We also express Vo P in a closed form, which, while not computationally recommended because it involves sparse, Kronecker-product, matrices, may be analytically useful. We consider a higher-order problem put into first-order, state-space, form. Present results can be used with Euler-equation solution methods after making the appropriate translation (Hansen and Sargent, 1981, pp. 134-135).