Minimizing post-shock forecasting error through aggregation of outside information

Jilei Lin; Daniel J. Eck

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arXiv · 2008.11756

Minimizing post-shock forecasting error through aggregation of outside information

Jilei Lin·Daniel J. Eck

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Abstract

We develop a forecasting methodology for providing credible forecasts for time series that have recently undergone a shock. We achieve this by borrowing knowledge from other time series that have undergone similar shocks for which post-shock outcomes are observed. Three shock effect estimators are motivated with the aim of minimizing average forecast risk. We propose risk-reduction propositions that provide conditions that establish when our methodology works. Bootstrap and leave-one-out cross validation procedures are provided to prospectively assess the performance of our methodology. Several simulated data examples, and a real data example of forecasting Conoco Phillips stock price are provided for verification and illustration.

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Jilei Lin, Daniel J. Eck. 2020-08-26. Minimizing post-shock forecasting error through aggregation of outside information. https://arxiv.org/abs/2008.11756

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