Consistent estimation of models defined by conditional moment restrictions

• 成果类型: Article
• 署名作者: Domínguez, MA; Lobato, IN
• 署名单位: Instituto Tecnologico Autonomo de Mexico
• 刊物名称: ECONOMETRICA
• ISSN/ISSBN: 0012-9682
• DOI: 10.1111/j.1468-0262.2004.00545.x
• 发表日期: 2004
• 页码: 1601-1615
• 关键词: STOCHASTIC PROCESSES; CONVERGENCE; likelihood; checks; form

摘要

In econometrics, models stated as conditional moment restrictions are typically estimated by means of the generalized method of moments (GMM). The GMM estimation procedure can render inconsistent estimates since the number of arbitrarily chosen instruments is finite. In fact, consistency of the GMM estimators relies on additional assumptions that imply unclear restrictions on the data generating process. This article introduces a new, simple and consistent estimation procedure for these models that is directly based on the definition of the conditional moments. The main feature of our procedure is its simplicity, since its implementation does not require the selection of any user-chosen number, and statistical inference is straightforward since the proposed estimator is asymptotically normal. In addition, we suggest an asymptotically efficient estimator constructed by carrying out one Newton-Raphson step in the direction of the efficient GMM estimator.