Identifying Number of Factors in Dynamic Factor Models Contributing to GDP Nowcasting: Bayesian Approach with Horse Shoe Shrinkage

• Activity Number: 167 - Data Mining and Econometrics
• Type: Contributed
• Date/Time: Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
• Sponsor: Business and Economic Statistics Section
• Author(s): Jiayi Luo* and Cindy Yu
• Companies: Department of Statistics, Iowa State University and Iowa State University
• Keywords: Dynamic Factor Models; Horse Shoe shrinkage; Bayesian Analysis; Nowcasting; Markov Chain Monte Carlo

Abstract

Real-time nowcasting is a process to assess current-quarter GDP from timely released economic and financial series before the figure is disseminated in order to catch the overall macroeconomic conditions in real-time. In economic data nowcasting, dynamic factor models (DFMs) are widely used due to their ability to bridge information with different frequencies and to achieve dimension reduction. However, most of the research using DFMs assumes a fixed known number of factors contributing to GDP nowcasting. In this paper, we propose a Bayesian method with Horse Shoe shrinkage prior to identifying the number of factors that have nowcasting power in GDP and to accurately estimate model parameters and latent factors simultaneously. The horseshoe prior is a powerful shrinkage prior in that it can shrink unimportant signals to 0 while keeping important ones remain large and practically unshrunk. We demonstrate the validity of our approach through simulation studies and an empirical study using US quarterly GDP data.