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We derive generalization error bounds for traditional time-series forecastingmodels. Our results hold for many standard forecasting tools includingautoregressive models, moving average models, and, more generally, linearstate-space models. These non-asymptotic bounds need only weak assumptions onthe data-generating process, yet allow forecasters to select among competingmodels and to guarantee, with high probability, that their chosen model willperform well. We motivate our techniques with and apply them to standardeconomic and financial forecasting tools---a GARCH model for predicting equityvolatility and a dynamic stochastic general equilibrium model (DSGE), thestandard tool in macroeconomic forecasting. We demonstrate in particular howour techniques can aid forecasters and policy makers in choosing models whichbehave well under uncertainty and mis-specification.

Nonparametric risk bounds for time-series forecasting