Economic Forecasting

Catalog description

The objective of the course is to introduce you to time series analysis and forecasting methods. Students will master a mix of theoretical and applied econometrics techniques used in macroeconomic and financial applications. Topics to be covered potentially include but are not limited to: regression from a predictive viewpoint; forecasting trends and seasonality; exponential smoothing models; ARMA models; stochastic trends, unit roots, and cointegration; structural breaks; point, interval and density forecasts; forecast evaluation and combination; vector autoregression including impulse-response estimation and analysis; dynamic factor models; volatility forecasting using GARCH models; conditional forecasting models and scenario analysis. The course emphasizes hands-on experience, and all students will acquire knowledge of the programming language R in the context of time series models and forecasting. Prerequisites: ECON 102B. Students with a strong background in Statistics may reach out to the Economics Undergraduate office for permission to enroll.