Macroeconomic concepts such as in ation and real economic activity are not directly observed. Researchers often use factor models in order to measure these unobserved concepts. The underlying view is that a small number of factors exist which represent the concept and drive many related variables. Consequently, the U.S. economy is often modeled as an a ne function of some factors. If indeed there is such a factor structure for the U.S. economy, then it can be represented by a generalized dynamic factor model (GDFM). In the rst chapter, I describe and summarize the literature on GDFMs. In the second chapter, I investigate the interactions and mutually independent dynamics of changes in in ation and real growth by applying the GDFM to a block of real growth variables, a block of in ation variables, and to their joint panel. In this manner, an empirical decomposition of the U.S. economy is obtained and this allows the reconcilitaion of forward and backward looking Phillips curves. In the third chapter, I build and study a discrete time generalized dynamic a ne term structure model. This is characterized by three main features that are conceptually important for a ne yield curve models. I allow: (a) for state vector dynamics beyond Markovian types; (b) that all yields may contain an idiosyncratic component to re ect measurement-errors in the data; and (c) that idiosyncratic components may be crosssectional as well as time-serial correlated. It is possible to directly compare this model with the version that is restricted by Du e-Kan's no-arbitrage conditions. Chapter four addresses whether or not changes in yields can be explained by changes to the latent dynamic factors which underlie the macroeconomic concepts of in ation and real growth. As such, I contribute to the debate about whether or not monetary policy should react to real activity measures.