Airline delays lead to a tremendous loss of time and resources and cost billions of dollars every year in the United States (U.S.). At certain times, individual airports become bottlenecks within the National Airspace System (NAS). To explore solutions for reducing the delay, it is essential to understand factors causing flight delay and its impact on airports in the NAS. Major causal factors of flight delay at airports include over-scheduling, en-route convective weather, reduced ceiling and visibility around airports, and upstream delay propagation. Delay at one airport can be passed on to other airports in the NAS, in another word, operational improvement at one airport will have network effect and benefit to other airports as well. Moreover delay at different airports in a region might agglomerate to cause delay at different regions in the NAS. Hence, to optimally allocate NAS resources, e.g. capital investment for airport capacity expansion, the impact of single airport delay to the NAS and vice versa need to be investigated and quantified. For air transportation planning and policy purposes, this study concentrates on providing answers from a macroscopic point of view without being distracted by volatile operational details. In the first part, we estimate the interaction between flight delay at one single airport and delay at the rest of the NAS (RNAS) using case study for LaGuardia (LGA) and Chicago O'Hare (ORD) airports. In the second part, this research applies multivariate simultaneous regression models to quantify airport delay spillover effects across 34 of the 35 Operational Evolution Plan (OEP) airports and the RNAS. Observing the interactions between these two models, they are regressed with an econometric technique; three stage least square (3SLS). Thus, the regression results help us to determine the delay interactions between different airports and the RNAS and compare these airports based on delay propagation characteristics. Another significant contribution of this research is that, the estimated coefficients can be used for determining the marginal effects of all the delay causal factors presented in the model. Also, regional airport system development has been a hot topic of research in the air transportation community in recent years. Many metropolitan regions are served with more than one airport making their operations synchronized and interdependent and are known as regional airport system. This paper studies nine different prospective regions with multi-airport systems in the U.S. and identifies various key factors affecting the delay in these regions. Econometrics models and three stage least square (3SLS) estimation method are used to explore interdependency of delay at the multi-airport system and the RNAS. Along with it, different factors affecting delay at the system and the RNAS is being identified from the research. The outcomes from this research will help aviation planners understand the spillover effects of delays from multi-airport systems and provide decision support for future NAS improvement.