Stationary Ultra Long Run Component by Jihyun Kim

Join us for this weeks Econometrics Research Seminar, Spring Term 2024

Jihyun Kim, from Sungkyunkwan University and the Toulouse School of Economics, will present their research on Stationary Ultra Long Run Component.

Abstract

To address the long run predictability puzzle and enable long run predictions, we introduce nonlinear dynamic models. In these models, observations are expressed as a nonlinear function of two key components: a stationary process representing the short term dynamics and an ultra long run (ULR) component. The ULR component is derived from an underlying stationary diffusion process through an 'infinite' change of time unit, resulting in a stationary local-to-unity model. We outline the construction of suitable autocorrelation functions and the process of filtering the short run and ULR components. Additionally, we introduce an estimation approach for the dynamic model of both the short term and ULR components using pairwise likelihood methods. We also highlight the impossibility to get accurate long run predictions even with a large number of observations. This challenge is illustrated by a stochastic volatility-in-mean model.

This seminar will be held on campus. This event is open to all levels of study and is also open to the public. To register your place and gain access to the webinar, please contact the seminar organisers.

This event is part of the Econometrics Research Seminar Series.