This is the first half of the second course in the econometrics sequence for first-year, finance PhD students. We will start by reviewing basic time series concepts and the asymptotic distribution theory associated with estimating well behaved time series models. Then, we will cover the generalized method of moments. Vector autoregressions are next, followed by maximum likelihood estimation and modeling conditional heteroskedasticity. We will then cover non-stationary time series including unit root econometrics and cointegration, and we will finish with an introduction to Bayesian analysis and the Kalman filter.