Golden Rule of Forecasting: Be conservative
J. Armstrong (),
Kesten Green () and
MPRA Paper from University Library of Munich, Germany
This paper proposes a unifying theory of forecasting in the form of a Golden Rule of Forecasting. The Golden Rule is to be conservative. A conservative forecast is consistent with cumulative knowledge about the present and the past. To be conservative, forecasters must seek all knowledge relevant to the problem, and use methods that have been validated for the situation. A checklist of 28 guidelines is provided to implement the Golden Rule. This article’s review of research found 150 experimental comparisons; all supported the guidelines. The average error reduction from following a single guideline (compared to common practice) was 28 percent. The Golden Rule Checklist helps forecasters to forecast more accurately, especially when the situation is uncertain and complex, and when bias is likely. Non-experts who know the Golden Rule can identify dubious forecasts quickly and inexpensively. To date, ignorance of research findings, bias, sophisticated statistical procedures, and the proliferation of big data have led forecasters to violate the Golden Rule. As a result, despite major advances in forecasting methods, evidence that forecasting practice has improved over the past half-century is lacking.
Keywords: accuracy; analytics; bias; big data; causal forces; causal models; combining; complexity; contrary series; damped trends; decision-making; decomposition; Delphi; ethics; extrapolation; inconsistent trends; index method; judgmental bootstrapping; judgmental forecasting; nowcasting; regression; risk; shrinkage; simplicity; stepwise regression; structured analogies (search for similar items in EconPapers)
JEL-codes: C01 C1 C15 C2 C3 C4 C5 C8 C9 K2 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-ore and nep-sog
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Journal Article: Golden rule of forecasting: Be conservative (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:53579
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