Long-term prediction intervals of economic time series

M. Chudý (), S. Karmakar and W. B. Wu
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M. Chudý: Ministry of Finance
S. Karmakar: University of Florida
W. B. Wu: University of Chicago

Empirical Economics, 2020, vol. 58, issue 1, No 9, 222 pages

Abstract: Abstract We construct long-term prediction intervals for time-aggregated future values of univariate economic time series. We propose computational adjustments of the existing methods to improve coverage probability under a small sample constraint. A pseudo-out-of-sample evaluation shows that our methods perform at least as well as selected alternative methods based on model-implied Bayesian approaches and bootstrapping. Our most successful method yields prediction intervals for eight macroeconomic indicators over a horizon spanning several decades.

Keywords: Heavy-tailed noise; Long memory; Kernel quantile estimator; Stationary bootstrap; Bayes (search for similar items in EconPapers)
JEL-codes: C14 C15 C53 C87 E27 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s00181-019-01689-2

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