A0411
Title: Factor-augmented sparse MIDAS regression for nowcasting
Authors: Jonas Striaukas - Copenhagen Business School (Denmark) [presenting]
Abstract: GDP nowcasting commonly employs either sparse regression or a dense approach based on factor models, which differ in the way they extract information from high-dimensional datasets. This paper aims to investigate whether sparse regression of the outcome on both the covariables and the factors can improve nowcasts. We propose an estimator for a factor-augmented sparse regression model. The rates of convergence of the estimator are derived in a time series context, accounting for tau-mixing processes and fat-tailed distributions.The application of this new technique to nowcast US GDP growth reveals several key findings. Firstly, our sparse plus dense technique significantly improves the quality of nowcasts compared to both sparse and dense benchmarks over a period period from 2008 Q1 to 2022 Q2. This improvement is particularly pronounced during the COVID pandemic, indicating the model's ability to capture the specific dynamics introduced by the pandemic. Interestingly, our novel factor-augmented sparse method does not perform significantly better than sparse regression prior to the onset of the pandemic, suggesting that using only a few predictors is sufficient for nowcasting in more stable economic times.
