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This course is designed to provide univariate and multivariate time series models for forecasting in business and economics. Topics include ARMA, in-sample/out-of-sample forecasting techniques, model selection and forecast evaluation, VAR and causality, response function analysis and variance decomposition, unit roots and cointegration, state space models, GARCH and volatility forecasting, and non-linear models. Forecasting is one of the most rapidly growing research areas with wide applicability in economics. The increasing complexity of global financial markets is fueling the demand for professional experts who possess an understanding of forecasting, econometric tools to solve forecasting problems, and necessary computer skills to create relevant forecasts. Forecasting combines the essential tools in economics, statistics, and mathematics to meet these growing needs. For the empirical work, econometric computer software will be studied. The recommended statistical software is Eviews, and the teaching assistant (TA) will guide and assist you to this computer software.