Panel Data Nowcasting

Abstract

This paper promotes the use of panel data in nowcasting. We shift the existing focus of the literature, which has almost exclusively used time series models to nowcast national aggregate variables like gross domestic product (GDP). We propose a mixed-frequency panel VAR model and a bias-corrected least squares (BCLS) estimator which attenuates the bias inherent to fixed effects dynamic panel settings. We demonstrate how existing panel model selection and combination methods can be adapted to the mixed-frequency setting with different lag specifications. Detailed Monte Carlo simulations find that these methods outperform naive approaches. We present a novel application of our methods to the case of nowcasting quarterly U.S. state-level real GDP using timely employment data. Our results indicate that utilising relevant monthly information is important, while also highlighting the gains from pooling information across states and from the use of appropriate lag selection or combination methods. We also find particularly large gains from nowcasting in states such as California; a region which has higher real GDP than most developed economies and deserves rigorous attention.

Keywords: Panel Data, Nowcasting, Model Selection, Model Averaging, State-level GDP

JEL Classification: C23, C52, C53