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. In this dissertation, we first develop a Bayesian approach to provide a way to deal with unbalanced feature of the data set and to estimate latent common factors when the number of factors is assumed to be fixed and known. Then we extend our method such that it can 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 the unbalanced data, which is typical of a real-time nowcasting analysis. We demonstrate the validity of our approach through simulation studies and explore the applicability of our approach through empirical studies in nowcasting China's GDP or US GDP using monthly data series of several categories in each country's market respectively. The simulation studies and empirical studies indicate that our Bayesian approach is a viable option to conduct real-time nowcasting for China's and US's quarterly GDP.