Nowcasting GDP Using Dynamic Factor Model with Unknown Number of Factors and Stochastic Volatility: A Bayesian Approach

Abstract

Real-time nowcasting is an assessment of current economic conditions from timely released economic series (such as monthly macroeconomic data) before the direct measure (such as quarterly GDP figure) is disseminated. Dynamic factor models (DFMs) are widely used in econometrics to bridge series with different frequencies and achieve a reduction in dimensionality. However, most of the research using DFMs often assumes the number of factors is known. A Bayesian approach is developed to identify the unknown number of factors and estimate the latent dynamic factors of DFMs accurately in a real-time nowcasting framework. The proposed method can deal with unbalanced data, which is typical of a real-time nowcasting analysis. Furthermore, the particle Gibbs with backward simulation algorithm is considered to obtain estimated stochastic volatility (SV) in monthly series efficiently. The validity of the method is demonstrated through simulation studies and an empirical study of nowcasting US’s quarterly GDP growth using monthly data series of several categories in the US market. Both the simulation and empirical studies indicate that the proposed Bayesian approach is a viable option to conduct real-time nowcasting for the US’s quarterly GDP.