Statistical Inference of Models with Time-Varying Parameters

University Of Chicago, Chicago IL

Investigators

Abstract

The investigator studies estimation, testing and construction of simultaneous confidence regions for time-varying parameters in models with non-stationary and dependent errors. The simultaneous confidence regions can be used to test patterns of associations between covariates and responses. The investigator also develops new tools for asymptotic analysis of non-stationary time series. During the last decade, models with time-varying data generating mechanisms have gained substantial attention in various fields including economics, finance, engineering, sociology, medical science, environmental science among others. Results from the proposal are useful for understanding the dynamic association between explanatory variables and responses for temporally observed data in which the underlying physical mechanism change with respect to time.