Accurate inference in high-dimensional econometrics

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.