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Is there a golden rule?

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Is there a golden rule? / Fildes, Robert; Petropoulos, Fotios.
In: Journal of Business Research, Vol. 68, No. 8, 08.2015, p. 1742-1745.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Fildes, R & Petropoulos, F 2015, 'Is there a golden rule?', Journal of Business Research, vol. 68, no. 8, pp. 1742-1745. https://doi.org/10.1016/j.jbusres.2015.01.059

APA

Fildes, R., & Petropoulos, F. (2015). Is there a golden rule? Journal of Business Research, 68(8), 1742-1745. https://doi.org/10.1016/j.jbusres.2015.01.059

Vancouver

Fildes R, Petropoulos F. Is there a golden rule? Journal of Business Research. 2015 Aug;68(8):1742-1745. Epub 2015 Apr 11. doi: 10.1016/j.jbusres.2015.01.059

Author

Fildes, Robert ; Petropoulos, Fotios. / Is there a golden rule?. In: Journal of Business Research. 2015 ; Vol. 68, No. 8. pp. 1742-1745.

Bibtex

@article{9394bab4d71a4be29775c61a41725428,
title = "Is there a golden rule?",
abstract = "Armstrong, Green, and Graefe (this issue) propose the Golden Rule in forecasting: “be conservative”. According to the authors, the successful application of the Golden Rule comes through a checklist of 28 guidelines. Even if the authors of this commentary embrace the main ideas around the Golden Rule, which targets to address the “average” situation, they believe that this rule should not be applied automatically. There is no universal extrapolationmethod that can tackle every forecasting problem; nor are there simple rules that automatically apply without reference to the data. Similarly, it is demonstrated that for a specific causal regression model the recommendedconservative rule leads to unnecessary inaccuracy. In this commentary the authors demonstrate, using the power of counter examples, two cases where the Golden Rule fails. Forecasting performance is context dependentand, as such, forecasters (researchers and practitioners) should take into account the specific features of the situation faced.",
keywords = "forecasting, Time series, ARIMA, Regression modelling, Forecasting accuracy, model specification, complexity ",
author = "Robert Fildes and Fotios Petropoulos",
year = "2015",
month = aug,
doi = "10.1016/j.jbusres.2015.01.059",
language = "English",
volume = "68",
pages = "1742--1745",
journal = "Journal of Business Research",
issn = "0148-2963",
publisher = "Elsevier Inc.",
number = "8",

}

RIS

TY - JOUR

T1 - Is there a golden rule?

AU - Fildes, Robert

AU - Petropoulos, Fotios

PY - 2015/8

Y1 - 2015/8

N2 - Armstrong, Green, and Graefe (this issue) propose the Golden Rule in forecasting: “be conservative”. According to the authors, the successful application of the Golden Rule comes through a checklist of 28 guidelines. Even if the authors of this commentary embrace the main ideas around the Golden Rule, which targets to address the “average” situation, they believe that this rule should not be applied automatically. There is no universal extrapolationmethod that can tackle every forecasting problem; nor are there simple rules that automatically apply without reference to the data. Similarly, it is demonstrated that for a specific causal regression model the recommendedconservative rule leads to unnecessary inaccuracy. In this commentary the authors demonstrate, using the power of counter examples, two cases where the Golden Rule fails. Forecasting performance is context dependentand, as such, forecasters (researchers and practitioners) should take into account the specific features of the situation faced.

AB - Armstrong, Green, and Graefe (this issue) propose the Golden Rule in forecasting: “be conservative”. According to the authors, the successful application of the Golden Rule comes through a checklist of 28 guidelines. Even if the authors of this commentary embrace the main ideas around the Golden Rule, which targets to address the “average” situation, they believe that this rule should not be applied automatically. There is no universal extrapolationmethod that can tackle every forecasting problem; nor are there simple rules that automatically apply without reference to the data. Similarly, it is demonstrated that for a specific causal regression model the recommendedconservative rule leads to unnecessary inaccuracy. In this commentary the authors demonstrate, using the power of counter examples, two cases where the Golden Rule fails. Forecasting performance is context dependentand, as such, forecasters (researchers and practitioners) should take into account the specific features of the situation faced.

KW - forecasting

KW - Time series

KW - ARIMA

KW - Regression modelling

KW - Forecasting accuracy

KW - model specification

KW - complexity

U2 - 10.1016/j.jbusres.2015.01.059

DO - 10.1016/j.jbusres.2015.01.059

M3 - Journal article

VL - 68

SP - 1742

EP - 1745

JO - Journal of Business Research

JF - Journal of Business Research

SN - 0148-2963

IS - 8

ER -