Using structural break inference for forecasting time series

Gantungalag Altansukh and Denise Osborn

Empirical Economics, 2022, vol. 63, issue 1, No 1, 41 pages

Abstract: Abstract Rather than relying on a potentially poor point estimate of a coefficient break date when forecasting, this paper proposes averaging forecasts over sub-samples indicated by a confidence interval or set for the break date. Further, we examine whether explicit consideration of a possible variance break and the use of a two-step methodology improves forecast accuracy compared with using heteroskedasticity robust inference. Our Monte Carlo results and empirical application to US productivity growth show that averaging using the likelihood ratio-based confidence set typically performs well in comparison with other methods, while two-step inference is particularly useful when a variance break occurs concurrently with or after any coefficient break.

Keywords: Forecasting time series; Structural breaks; Confidence intervals; Combining forecasts; Productivity growth; C32; C53 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00181-021-02137-w

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