The covers methodologies appropriate to the analysis of financial data as it is associated with advanced time series modeling including systems of equations, simultaneous equations and limited dependent variable models. These approaches include panel estimation procedures, autoregressive moving average models, generalized autoregressive conditional heteroskedasticity, vector autoregression and vector error correction. The multiple implications of random walks are examined in detail, including problems associated with difference stationarity, trend stationarity, and noise modeling. Prerequisite: FIN 6335.