Using the bootstrap to evaluate forecasting equations

David A. Freedman

32 citations0/0 files storedJournal of Forecasting

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Forecasting Techniques and Applications Statistical Methods and Inference Advanced Statistical Methods and Models

Abstract

Abstract The bootstrap, like the jack‐knife, is a technique for estimating standard errors. The idea is to use Monte Carlo simulation, based on a non‐parametric estimate of the underlying error distribution. The bootstrap will be applied to an econometric equation describing the demand for energy by industry, to determine multi‐period forecasting error and choose among competing specifications. The delta method for estimating forecast errors turns out to be too optimistic by a factor of 2.

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References

1973A Statistical Theory of Calibration 1980Estimating the Expected Predictive Accuracy of Econometric Models 1979An Analysis of the Accuracy of Four Macroeconometric Models 1974The Asymptotic Distribution of Forecasts in the Dynamic Simulation of an Econometric Model 1983On Energy Policy Models

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