Asymptotic least‐squares estimation efficiency considerations and applications
研究渐近最小二乘估计量在大样本下的效率，讨论加入额外信息对效率的影响，并应用于荷兰制造业的动态理性预期要素需求模型，展示其在非线性约束参数估计中的潜力。
Abstract This paper is concerned with the large sample efficiency of the asymptotic least‐squares (ALS) estimators introduced by Gouriéroux, Monfort, and Trognon (1982, 1985) and Chamberlain (1982, 1984). We show how the efficiency of these estimators is affected when additional information is incorporated into the estimation procedure. The relationship between ALS and maximum likelihood is discussed. It is shown that ALS can be used to obtain asymptotically efficient estimates for a large range of econometric models. Many results from the literature on estimation are special cases of the framework adopted in this paper. An application of ALS to a dynamic rational expections factor demand model in the manufacturing sector in The Netherlands demonstrates the potential of the method in the estimation of the parameters in models which are subject to nonlinear cross‐equation restrictions.