This paper proposes a tripartite framework of design, evaluation, and post-evaluation analysis for generating and interpreting economic forecasts. This framework's value is illustrated by re-examining mean square forecast errors from dynamic models and nonlinearity biases from empirical...

This paper provides an introduction to predictable forecast uncertainty in empirical economic modelling. The sources of both predictable and unpredictable forecast uncertainty are categorized. Key features of predictable forecast uncertainty are illustrated by several analytical models,...

Parameter constancy and a model's mean square forecast error are two commonly used measures of forecast performance. By explicit consideration of the information sets involved, this paper clarifies the roles that each plays in analyzing a model's forecast accuracy. Both criteria are necessary...

This paper provides an introduction to forecast uncertainty in empirical economic modeling. Forecast uncertainty is defined, various measures of forecast uncertainty are examined, and some sources and consequences of forecast uncertainty are analyzed. Empirical illustrations with the U.S. trade...

Both future disturbances and estimated coefficients contribute to the uncertainty in model-based ex ante forecasts, but only the first source is usually taken into account when calculating confidence intervals for practical applications. Schmidt (1974) and Baillie (1979) provide an easily...

This paper evaluates the distributional properties of forecasts from six econometric models of the U.S. trade account. Using stochastic (Monte Carlo) simulation, we derive confidence intervals and forecast-based test statistics which account for uncertainty from future disturbances and from...

We consider forecasting with factors, variables and both, modeling in-sample using Autometrics so all principal components and variables can be included jointly, while tackling multiple breaks by impulse-indicator saturation. A forecast-error taxonomy for factor models highlights the impacts of...

To explain which methods might win forecasting competitions on economic time series, we consider forecasting in an evolving economy subject to structural breaks, using mis-specified, data-based models. `Causal' models need not win when facing deterministic shifts, a primary factor underlying...