Topics in Econometric Methods

9730277安德鲁斯这个项目涉及计量经济学的三个不同领域的研究：(1)非标准情景下的极值估计器。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成立的事实，至少是渐近成立的。然而，有许多有趣的情况，其中真实参数在参数空间的边界上。这个项目开发了测试、模型选择、自举和二次抽样程序以及贝叶斯渐近性的方法，用于解决这个标准假设不再成立的问题。(2)广义矩方法(GMM)的矩和模型选择。使用GMM的实证研究人员经常发现，并不是所有的矩条件都是正确的。同时，通常的情况是，研究人员对感兴趣的模型的精确规范有一定的不确定性。例如，他们可能不知道要在模型中包括变量的滞后多少，或者变量是否应该包括在回归中。这个项目开发了GMM估计器的选择程序，同时选择正确的矩和正确的模型规格。这些模型/矩选择程序被应用于具有未观察到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)加速偏差修正的可信区间。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，但确定要使用的自举重复次数是现有文献中的一个常见问题。通常，这个数字是以某种特殊的方式确定的。这是有问题的，因为如果引导重复的次数太少，只需使用不同的模拟绘图就可以从相同的数据中获得不同的答案。该项目开发了一种确定加速偏差校正可信区间的自举重复次数的方法。?？

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. ??