A1575
Title: Functional regression with nonstationarity and error contamination: Application to the economic impact of climate change
Authors: Won-Ki Seo - University of Sydney (Australia) [presenting]
Abstract: A regression model with functional dependent and explanatory variables exhibiting nonstationary dynamics is studied. The model assumes that the nonstationary stochastic trends of the dependent variable are explained by those of the explanatory variable, and hence that a stable long-run relationship exists between the two variables despite their nonstationary behavior. Functional observations may be error-contaminated. Novel autocovariance-based estimation and inference methods are developed for this model. The methodology is broadly applicable to economic and statistical functional time series with nonstationary dynamics. Application to evaluating the global economic impact of climate change demonstrates the methodology's usefulness and addresses an issue of intrinsic importance.
