Accurate inference in high-dimensional econometrics

近几十年来，更大数据集的可用性导致研究人员采用所谓的“高维”模型，即包括大量变量和参数的模型。这使得在实证经济研究中调查个人决策的潜在决定因素时，可以考虑更大的个人和环境特征。然而，具有大量参数的计量经济学模型具有特定的统计特性。出于这个原因，大量的文献最近集中在建立高维设置的统计程序。虽然最近已经建立了计量经济学中估计高维模型的方法，但在这些环境中如何进行统计推断仍然是未知的。我提出的研究旨在建立这类模型的统计检验方法。更具体地说，我打算得出一个概括的强大的推理程序，以高维设置的主力实证经济研究，线性回归模型。此外，我还打算在我的硕士学位论文的基础上，为高维非线性面板数据模型的统计推断方法的性质提供更多的证据。我提出的研究将有助于理论计量经济学的一个日益增长的领域，并将为实证经济研究中越来越多的可用数据的分析提供重要的方法论贡献。

In recent decades, the availability of larger datasets has led researchers to employ so-called "high-dimensional" models, that is models that include a large number of variables and parameters. This allows to account for a much bigger set of individual and environmental characteristics when investigating the underlying determinants of individual decisions in empirical economic research. However, econometric models with a high number of parameters feature specific statistical properties. For this reason, a large body of literature has recently focused on establishing statistical procedures in high-dimensional settings. While methods to estimate high-dimensional models in econometrics have been recently established, it remains largely unknown how statistical inference should be carried out in these settings. My proposed research aims at establishing methods for statistical testing in this type of models. More specifically, I intend to derive a generalisation of robust inference procedures to high-dimensional settings for the workhorse of empirical economic research, the linear regression model. Moreover, I also intend to build on my MSc dissertation to provide additional evidence on the properties of statistical inference methods for high-dimensional nonlinear panel data models.My proposed research would contribute to a growing field in theoretical econometrics, and would provide an important methodological contribution to the analysis of the increasing amount of available data in empirical economic research.