UC San Diego

Economists increasingly use nonlinear methods to confront their theories with data. The switch from linear to nonlinear methods is driven, in part, by increased computing power, but also by a desire to understand economic phenomena that cannot easily be captured by linear models. My research is informed by questions at the intersection of macroeconomics and finance that cannot be addressed with standard methods.

Existing methods for estimating nonlinear dynamic models are either too computationally complex to be of practical use, or rely on local approximations which fail to adequately capture the nonlinear features of interest. My research develops a new methodology for accurately estimating nonlinear dynamic models which is computationally simple and easy to apply. In my dissertation, I apply this methodology to study a model of interest rate dynamics near the zero lower bound, an asset pricing model of rare disasters, and a model of learning about cash flows in the presence of structural change.