Some current topics in conditional moment equations models: generated regressors, unknown nuisance functions and panel data

条件矩方程模型中的一些当前主题：生成的回归量、未知的干扰函数和面板数据

• 批准号: 386140326
• 负责人: Professor Dr. Alois Kneip
• 金额: --
• 依托单位: Statistische Abteilung
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2017
• 资助国家: 德国
• 起止时间: 2016-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/386140326?language=en
• 关键词: Some; current; topics; conditional; moment

Models in economics are frequently defined by conditional moment conditions. By far the most popular estimation method for this kind of models is GMM. Over the last decades several new estimation procedures have been developed that try to incorporate more of the information entailed in conditional moment conditions in order to avoid the loss of identification, a major risk incurred when using GMM. In addition, some of the new models introduce unknown nuisance functions in the moment conditions, and such models require specific semiparametric estimation methods. Most of the modern methods used for estimation and inference in conditional moment equations models require user-chosen tuning parameters whose influence on the estimates remains widely unexplored. Due to the importance of expectations and the impact of uncertainty in the determinationof economic relationships the use of generated regressors in economic models has increased. Examples of generated regressors are expectations formed by individuals for prices or inflation. If these variables are used in the model the quantities need to be estimated in a first step and are, thus, random variables available with some error.The challenge we face here is to study the so-called SmoothMD estimator in models identified by conditional moment conditions in the presence of unknown functional parts and generated regressors. This estimator is based on one unconditional moment equation that guarantees identification. By combining SmoothMD with generated regressors and unknown functional form, we also propose to get a higher degree of insight into the influence of user-chosen parameters in these models. The unconditional moment equation initially used in SmoothMD contains an unknown instrument function that has to be estimated. It could be estimated by smoothing with possibly a data driven user-chosen parameter, or by a simple empirical mean, which corresponds to a fixed user-chosen smoothing parameter. Hence, in principle the method allows to control for the influence of the user-chosen parameter required to estimate the instrument function. In addition, we want to extend SmoothMD estimation to panel data models.So far SmoothMD does not allow for panel data sets as an i.i.d. assumption is imposed. As panel data is more and more available and may contain more information then cross-sectional or time series data sets, we consider the extension of SmoothMD to such models as valuable. The methodological development well be guided by an important application. We plan to study the influence of regressors on some outcome variable when some regressors are endogenous and the functional relationship between the endogenous regressors and the dependent variable is not completely known. This is a semiparametric instrumental variable approach. We consider this as interesting as IV estimation is frequently employed in econometrics and nonlinear relationships are considered more and more often.

经济学中的模型通常是由条件力矩条件定义的。到目前为止，这种模型最受欢迎的估计方法是GMM。在过去的几十年中，已经开发了几种新的估计程序，试图将更多的信息纳入条件时刻条件中，以避免识别丢失，这是使用GMM时发生的主要风险。此外，一些新模型在瞬间条件下引入了未知的滋扰功能，此类模型需要特定的半参数估计方法。用于估算和推断的大多数现代方法模型都需要用户选择的调谐参数，这些调整参数对估计值的影响仍未得到广泛探索。由于期望的重要性以及不确定性在经济关系的确定中的影响，因此在经济模型中使用产生的回归器的使用增加了。产生的回归者的示例是个人对价格或通货膨胀形成的期望。如果在模型中使用了这些变量，则需要在第一步中估算数量，因此，在某些错误中可以使用随机变量。我们在这里面临的挑战是在存在未知功能零件和生成的回归器的情况下，在有条件矩情况下确定的模型中所谓的SmoothMD估计器。该估计器基于保证识别的一个无条件力矩方程。通过将SmoothMD与生成的回归器和未知功能形式相结合，我们还建议在这些模型中对用户选择参数的影响更加了解。 SmoothMD最初使用的无条件矩方程包含必须估算的未知仪器函数。可以通过使用数据驱动的用户选择的参数或简单的经验平均值来估计它，该参数对应于固定的用户选择的平滑参数。因此，原则上，该方法允许控制估计仪器功能所需的用户选择参数的影响。此外，我们希望将平滑估计扩展到面板数据模型。因此，SmootyMD不允许将面板数据集作为I.I.D.实施假设。由于面板数据越来越可用，并且可能包含更多信息，而不是横截面或时间序列数据集，因此我们考虑将SmootyMD扩展到有价值的模型。方法论的发展受到重要应用的指导。我们计划研究一些回归因子是内源性的，并且内源性回归器与因变量之间的功能关系时，回归者对某些结果变量的影响并不完全清楚。这是一种半参数仪器变量方法。我们认为这很有趣，就像经常在计量经济学中使用IV估计，而非线性关系被越来越多地考虑。