Title: Large-scale dynamic predictive regressions
Authors: Daniele Bianchi - Queen Mary University of London (United Kingdom) [presenting]
Kenichiro McAlinn - Temple University (United States)
Abstract: A large-scale dynamic predictive strategy for forecasting an economic decision making in a data-rich environment is proposed and evaluated. Under this framework, clusters of predictors generate different predictive densities that are later synthesized within an implied time-varying latent factor model. We test our procedure by predicting both the inflation rate and the equity premium across different industries in the U.S., based on a large set of macroeconomic and financial variables. The main results show that our framework generates both statistically and economically significant out-of-sample outperformance compared to a variety of sparse and dense regression-based models while maintaining critical economic interpretability.