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. ??

小行星9730277 本研究课题涉及计量经济学三个不同领域的研究：（1）非标准情景下的方差估计。 估计量和检验统计量的一个标准假设是真实参数在参数空间的内部。 这个假设是方便的，因为它允许人们利用一阶条件至少渐近地成立的事实。 然而，有许多有趣的情况，其中真参数在参数空间的边界上。 这个项目开发的方法，测试，模型选择，引导和二次抽样程序和贝叶斯渐近的问题，这个标准的假设不再成立。 (2)广义矩量法的矩量和模型选择。 使用GMM的经验研究人员经常发现，并非所有的矩条件都是正确的。 与此同时，研究人员往往对感兴趣的模型的精确规格有一些不确定性。 例如，他们可能不知道模型中要包括变量的滞后量，或者是否应该将变量包括在回归量中。 这个项目开发选择程序的GMM估计，同时选择正确的时刻和正确的模型规格。 这些模型/矩的选择程序适用于动态面板数据模型与未观察到的个人影响，应用计量经济学的一个重要领域。 (3)加速偏倚校正置信区间。 Bootstrap方法在实证研究中得到了广泛的应用。 虽然这些方法很容易应用，但在现有的文献中，确定要使用的自举重复次数是一个常见的问题。 通常，这个数字是以某种特别的方式确定的。 这是有问题的，因为如果自举重复的次数太少，则可以仅通过使用不同的模拟绘制从相同的数据获得不同的答案。 本计画发展一种方法来决定加速偏差校正信赖区间的自助重复次数。 ??