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Time series forecasting: model evaluation and …
time series forecasting : model evaluation and selection usingnonparametric risk bounds Daniel J. McDonald ,CosmaRohillaShalizi ,andMarkSchervish Department of Statistics, Indiana University Bloomington Department of Statistics, Carnegie Mellon University Santa Fe InstituteVersion: December 2, 2012 AbstractWe derive generalization error bounds bounds on the expected inaccuracy of the predictions fortraditional time series forecasting models. Our results hold for many standard forecasting tools includingautoregressive models, moving average models, and, more generally, linear state-space models. Thesebounds allow forecasters to select among competing models and to guarantee that with high probability,their chosen model will perform well without making strong assumptions about the data generatingprocess or appealing to asymptotic theory. We motivate our techniques with and apply them to standardeconomic and financial forecasting tools a GARCH model for predicting equity volatility and a dynamicstochastic general equilibrium model (DSGE), the standard tool in macroeconomic forecasting .
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.
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