Forecasting by factors, by variables, by both, or neither?
نویسندگان
چکیده
We forecast US GDP and inflation over 1-, 4and 8-step horizons using the dataset from Stock and Watson (2009), with factors, variables, both, and neither. Autometrics handles perfect collinearity and more regressors than observations, enabling all principal components and variables to be included for model selection, jointly with using impulse-indicator saturation (IIS) for multiple breaks. Empirically, factor models are more useful for 1-step ahead forecasts than at longer horizons, when selecting over variables tends to be better. Accounting for in-sample breaks and outliers using IIS is useful. Recursive updating helps, but recursive selection leads to greater variability, and neither outperforms autoregressions. JEL classifications: C51, C22.
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