This course covers both theoretical and practical aspects of time series econometrics including the analysis of stationary and non-stationary stochastic processes in economics, business and finance. The students are introduced to well-known univariate time series models, such as autoregressive moving average (ARMA) models and error correction models (ECM), and learn how to judge their appropriateness for modelling real-life data sets. Moreover, the course provides both theoretical and practical insights into parameter estimation for time-series models and the use of these models for tasks that are crucial for many practitioners: e.g., forecasting, testing for Granger causality, and performing policy analysis using impulse response functions. Finally, students become familiar with the fundamental problem of spurious regression in time-series analysis. We find a solution to this problem by taking a journey into the theory and practice behind unit-root tests, cointegration tests and error-correction representation theorems. In this way, students will be able to disentangle possible short-term and long-term dynamics in time series data.