J. Eduardo Vera-Valdés
I am an Associate Professor at the Department of Mathematical Sciences at Aalborg University. Furthermore, I am a member of the National System of Researchers (SNI) of the Mexican National Council of Science and Technology (CONACYT), and a Research Fellow at CREATES -Center for Research in Econometric Analysis of Time Series-, and the Danish Finance Institute.
I obtained my PhD in Economics and Business Economics in 2016 at Aarhus University and CREATES.
My research interests are econometrics, time series, long memory, statistical learning, and climate econometrics. I have published in the Journal of Econometrics, and at Journal of Financial Econometrics, among others. Furthermore, I am a certified GitHub Campus Advisor.
I have served as a referee for the International Journal of Forecasting, the Journal of Time Series Analysis, the Journal of Computational and Graphical Statistics, Economics Letters, and for Computational Statistics & Data Analysis, among others. Moreover, I am one of the organizers of the Long Memory Conference and the Data Science Computing Conference.
Previously, I studied Mathematics at the Center for Mathematical Research (CIMAT) in 2007 in Guanajuato, Mexico, and obtained a Master’s Degree in Economics from the Center for Research and Teaching in Economics (CIDE) 2010, also in Mexico. I worked at Mexicos Central Bank and as Assistant Professor at the University of Guanajuato.
Session
This talk presents a package to analyse long-range dependence (LRD) in time series data. LRD is shown by the fact that the effects from previous disturbances take longer to dissipate than what standard models can capture. Failing to account for LRD dynamics can perversely affect forecasting performance: a model that does not account for LRD misrepresents the true prediction confidence intervals. LRD has been found in climate, political affiliation and finance data, to name a few examples.