Topics in Econometric Methods

计量经济学方法主题

• 批准号: 9730277
• 负责人: Donald Andrews
• 金额: $ 23.06万
• 依托单位: Yale University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-04-15 至 2002-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9730277&HistoricalAwards=false
• 关键词: Topics; Econometric; Methods

9730277 Andrews This project involves research in three different areas of econometrics: (1) Extremum Estimators in Non-Standard Scenarios. A standard assumption for estimators and test statistics is that the true parameter is in the interior of the parameter space. This assumption is convenient because it allows one to make use of the fact that first order conditions hold, at least asymptotically. There are numerous cases of interest, however, in which the true parameter is on the boundary of the parameter space. This project develops methods for testing, model selection, bootstrap and subsampling procedures and Bayesian asymptotics for problems where this standard assumption no longer holds. (2) Moment and Model Selection for the Generalized Method of Moments (GMM). Empirical researchers using GMM often find that not all moment conditions are correct. At the same time, it is often the case that researchers have some uncertainty regarding the precise specification of the model of interest. For example, they may not know how many lags of a variable to include in the model or whether a variable should be included in the regressor or not. This project develops selection procedures for GMM estimators that simultaneously select correct moments and correct model specifications. These model/moment selection procedures are applied to dynamic panel data models with unobserved individual effects, an important area of applied econometrics. (3) Accelerated Bias-Corrected Confidence Intervals. Bootstrap methods have gained a great deal of popularity in empirical research. Although the methods are easy to apply, determining the number of bootstrap repetitions to employ is a common problem in the existing literature. Typically, this number is determined in a somewhat ad hoc manner. This is problematic, because one can obtain a different answer from the same data merely by using different simulation draws if the number of bootstrap repetitions is too small. This project develops a method of determining the number of bootstrap repetitions for accelerated bias-corrected confidence intervals. ??

9730277 Andrews 该项目涉及计量经济学三个不同领域的研究：（1）非标准情景中的极值估计。 估计器和检验统计量的标准假设是真实参数位于参数空间的内部。 这种假设很方便，因为它允许人们利用一阶条件至少渐近成立的事实。 然而，有许多令人感兴趣的情况，其中真实参数位于参数空间的边界上。 该项目开发了测试、模型选择、引导和子采样程序以及贝叶斯渐近法的方法，以解决此标准假设不再成立的问题。 (2)广义矩法(GMM)的矩和模型选择。 使用 GMM 的实证研究人员经常发现并非所有矩条件都是正确的。 与此同时，研究人员通常对感兴趣模型的精确规范存在一些不确定性。 例如，他们可能不知道模型中应包含变量的滞后量，或者变量是否应包含在回归量中。 该项目开发了 GMM 估计器的选择程序，可同时选择正确的矩和正确的模型规格。 这些模型/矩选择程序应用于具有不可观察的个体效应的动态面板数据模型，这是应用计量经济学的一个重要领域。 (3) 加速偏差校正置信区间。 Bootstrap 方法在实证研究中广受欢迎。 尽管这些方法很容易应用，但确定要采用的引导重复次数是现有文献中的一个常见问题。 通常，这个数字是以某种临时方式确定的。 这是有问题的，因为如果自举重复次数太少，仅仅通过使用不同的模拟绘图就可以从相同的数据获得不同的答案。 该项目开发了一种确定加速偏差校正置信区间的引导重复次数的方法。 ??