This book presents modern developments in time series econometrics that are applied to macroeconomic and financial time series, bridging the gap between methods and realistic applications. It covers the most important approaches to the analysis of time series, which may be stationary or nonstationary. Modeling and forecasting univariate time series is the starting point. For multiple stationary time series, Granger causality tests and vector autoregressive models are discussed. As the modeling of nonstationary univariate or multivariate time series is crucial for real applied work, unit root and cointegration analysis, as well as vector error correction models, are central topics. Tools for analyzing nonstationary data are then adapted to the panel framework. Modeling the multivariate volatility of financial time series with autoregressive conditional heteroskedastic models is also considered.