Using data from government sources (FRED, BEA, BLS), the thesis explores the underlying reasons for declining U.S. economic growth. A long standing trend of annual 3% growth no longer seems to hold true for the economy. The paper summarizes current theory as to why the growth has slowed and finds new explanations by analyzing the various major industries which make up GDP. The results show that sectoral shifts in employment from high paying industries to low paying industries help to explain a significant portion of the decline in national growth rates. The decline in growth is primarily driven by about ten poor performing states.

This thesis develops a new Bayesian approach to structural break modeling. The focuses of the approach are the modeling of in-sample structural breaks and forecasting time series allowing out-of-sample breaks. Our model has some desirable features. First, the number of regimes is not fixed and is treated as a random variable in our model. Second, our model adopts a hierarchical prior for regime coefficients, which allows for the regime coefficients of one regime to contain information about regime coefficients of other regimes. However, the regime coefficients can be analytically integrated out of the posterior distribution and therefore we only need to deal with one level of the hierarchy. Third, the implementation of our model is simple and the computational cost is low. Our model is applied to two different time series: S&P 500 monthly returns and U.S. real GDP quarterly growth rates. We linked breaks detected by our model to certain historical events.