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• W2964286225 startingPage "1044" @default.
• W2964286225 abstract "We derive generalization error bounds for traditional time-series forecasting models. Our results hold for many standard forecasting tools including autoregressive models, moving average models, and, more generally, linear state-space models. These nonasymptotic bounds need only weak assumptions on the data-generating process, yet allow forecasters to select among competing models and to guarantee, with high probability, that their chosen model will perform well. We motivate our techniques with and apply them to standard economic and financial forecasting tools--a GARCH model for predicting equity volatility and a dynamic stochastic general equilibrium model (DSGE), the standard tool in macroeconomic forecasting. We demonstrate in particular how our techniques can aid forecasters and policy makers in choosing models which behave well under uncertainty and mis-specification." @default.
• W2964286225 created "2019-07-30" @default.
• W2964286225 creator A5021537000 @default.
• W2964286225 creator A5021966353 @default.
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• W2964286225 date "2017-01-01" @default.
• W2964286225 modified "2023-09-23" @default.
• W2964286225 title "Nonparametric risk bounds for time-series forecasting" @default.
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