Topics in Econometric Methods

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。这个假设很方便，因为它允许我们利用一阶条件成立的事实，至少是渐近成立的事实。然而，有许多令人感兴趣的情况，其中真参数位于参数空间的边界上。该项目开发了测试、模型选择、自举和子抽样程序以及贝叶斯渐近方法，用于解决这个标准假设不再成立的问题。(2)广义矩量法（GMM）的矩量与模型选择。使用GMM的实证研究人员经常发现并非所有的力矩条件都是正确的。同时，通常情况下，研究人员对感兴趣的模型的精确规格有一些不确定性。例如，他们可能不知道一个变量在模型中应该包含多少滞后，或者一个变量是否应该包含在回归器中。该项目开发了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/时刻选择程序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，因为如果引导重复的次数太少，仅仅通过使用不同的模拟绘制就可以从相同的数据中获得不同的答案。本项目开发了一种确定加速偏差校正置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. ??