Forecasting With Model Uncertainty: Representations and Risk Reduction

Keisuke Hirano, Jonathan H. Wright

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


We consider forecasting with uncertainty about the choice of predictor variables. The researcher wants to select a model, estimate the parameters, and use the parameter estimates for forecasting. We investigate the distributional properties of a number of different schemes for model choice and parameter estimation, including: in-sample model selection using the Akaike information criterion; out-of-sample model selection; and splitting the data into subsamples for model selection and parameter estimation. Using a weak-predictor local asymptotic scheme, we provide a representation result that facilitates comparison of the distributional properties of the procedures and their associated forecast risks. This representation isolates the source of inefficiency in some of these procedures. We develop a simulation procedure that improves the accuracy of the out-of-sample and split-sample methods uniformly over the local parameter space. We also examine how bootstrap aggregation (bagging) affects the local asymptotic risk of the estimators and their associated forecasts. Numerically, we find that for many values of the local parameter, the out-of-sample and split-sample schemes perform poorly if implemented in the conventional way. But they perform well, if implemented in conjunction with our risk-reduction method or bagging.

Original languageEnglish (US)
Pages (from-to)617-643
Number of pages27
Issue number2
StatePublished - Mar 1 2017


  • Model choice
  • Rao–Blackwell theorem
  • bagging
  • out-of-sample
  • shrinkage

ASJC Scopus subject areas

  • Economics and Econometrics


Dive into the research topics of 'Forecasting With Model Uncertainty: Representations and Risk Reduction'. Together they form a unique fingerprint.

Cite this