# Forecasting

This course provides an upper-level undergraduate introduction to forecasting, broadly defined, in economics and related fields.

Prerequisites: Background in (1) probability/statistics and (2) introductory econometrics. In addition, linear algebra (vector/matrix manipulations/calculations) will occasionally be used. You should either be comfortable with linear algebra or willing/able to pick it up as necessary.

Books: Mostly Diebold's Forecasting. (More generally, see below under Important Resources, and note that new material may be supplied piece-by-piece as we progress.)

Syllabus: Topics to be covered potentially include but are not at all limited to: regression from a predictive viewpoint; conditional expectations vs. linear projections; decision environment and loss function; the forecast object, statement, horizon and information set; the parsimony principle, relationships among point, interval and density forecasts; statistical graphics for forecasting; forecasting trends and seasonals; model selection for forecasting; characterizing, modeling and forecasting cycles with ARMA and related models; Wold’s theorem and the general linear process; nonlinearities and regime switching; the chain rule of forecasting; optimal forecasting under symmetric and asymmetric loss; recursive and related methods for diagnosing and selecting forecasting models; formal models of unobserved components; conditional forecasting models and scenario analysis ("stress testing"); vector autoregressions, predictive causality, impulse-response functions and variance decompositions; use of survey data; business cycle analysis using coincident and leading indicators: expansions, contractions, turning points, and leading indicators; incorporation of subjective information; Bayesian VARs and the Minnesota prior; evaluating a single forecast; comparing forecast accuracy; encompassing and forecast combination; combining forecasts; preliminary series, revised series, and the limits to forecast accuracy; prediction markets; unit roots, stochastic trends, stochastic trends and forecasting; unit roots; smoothing; ARIMA models, smoothers, and shrinkage; using stochastic-trend unobserved-components models to implement smoothing techniques in a probabilistic framework; cointegration and error correction; evaluating forecasts of integrated series; volatility forecasting via GARCH, stochastic volatility and realized volatility.

Software: EViews, R or Python, perhaps others. There are issues and tradeoffs, which will be discussed, and which you should consider before deciding what to do.