Judgmental Adjustment of Statistical Forecasts

Sanders, Nada R.; Ritzman, Larry P.

doi:10.1007/978-0-306-47630-3_18

Cited by 51 publications

(41 citation statements)

References 35 publications

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“…Adjustments should only be considered when the conditions for successful adjustment are met and when bias can be avoided (Goodwin and Fildes 1999;Fildes, Goodwin, Lawrence, and Nikolopoulos 2009). Judgmental adjustments of forecasts are best confined to experts' estimates of the effects of important influences not included in the forecasting model (Sanders and Ritzman 2001) such as when experts have good knowledge of the effects of special events and changes in causal forces (Fildes and Goodwin 2007). The estimates should be made in ignorance of the model forecasts, but with knowledge of what method and information the model is based upon (Armstrong and Collopy 1998;Armstrong, Adya, and Collopy 2001).…”

Section: Avoid Unstructured Judgmental Adjustments To Forecasts (6)mentioning

confidence: 99%

Golden Rule of Forecasting: Be Conservative

2015

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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. Nonexperts 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.

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Section: Avoid Unstructured Judgmental Adjustments To Forecasts (6)mentioning

confidence: 99%

Golden Rule of Forecasting: Be Conservative

2015

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“…It could also include a subjective estimate when experts have observed large recent changes, especially when they understand their causes and can thus judge whether the changes are temporary or permanent. This principle is based mostly on speculation, although Armstrong (1970), Sanders and Ritzman (2001), Tessier and Armstrong (1977), and Williams and Miller (1999) provide some empirical support. Such combinations can be automated.…”

Section: Principles Of Forecastingmentioning

confidence: 99%

“…Armstrong and Collopy (1998) found 47 empirical studies of such methods, all but four published since 1985. Webby, O'Connor and Lawrence (2001) and Sanders and Ritzman (2001) also examine this issue. Rule-based forecasting represents an attempt to integrate such knowledge (Armstrong, Adya and Collopy 2001).…”

Section: Implications For Researchersmentioning

confidence: 99%

Extrapolation for Time-Series and Cross-Sectional Data

Armstrong

2001

International Series in Operations Research &Amp; Management Science

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Extrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts' commonly held opinions) and on empirical studies. Some of the more important principles are:• In selecting and preparing data, use all relevant data and adjust the data for important events that occurred in the past.• Make seasonal adjustments only when seasonal effects are expected and only if there is good evidence by which to measure them.• In extrapolating, use simple functional forms. Weight the most recent data heavily if there are small measurement errors, stable series, and short forecast horizons. Domain knowledge and forecasting expertise can help to select effective extrapolation procedures. When there is uncertainty, be conservative in forecasting trends. Update extrapolation models as new data are received.• To assess uncertainty, make empirical estimates to establish prediction intervals.• Use pure extrapolation when many forecasts are required, little is known about the situation, the situation is stable, and expert forecasts might be biased. Disciplines Business | Marketing ABSTRACTExtrapolation methods are reliable, objective, inexpensive, quick, and easily automated. As a result, they are widely used, especially for inventory and production forecasts, for operational planning for up to two years ahead, and for long-term forecasts in some situations, such as population forecasting. This paper provides principles for selecting and preparing data, making seasonal adjustments, extrapolating, assessing uncertainty, and identifying when to use extrapolation. The principles are based on received wisdom (i.e., experts' commonly held opinions) and on empirical studies. Some of the more important principles are:

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“…1 See for example Lawrence et al (1985), McNees (1990), Reifschneider et al (1997), Sanders and Ritzman (1999), Svensson (2005) and Svensson and Tetlow (2005). However, the opposing viewpoint has some currency.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Judgement in Fan Charts

Österholm

2006

SSRN Journal

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Within a decision-making group, such as the monetary-policy committee of a central bank, group members often hold differing views about the future of key economic variables. Such differences of opinion can be thought of as reflecting differing sets of judgement. This paper suggests modelling each agent's judgement as one scenario in a macroeconomic model. Each judgement set has a specific dynamic impact on the system, and accordingly, a particular predictive density -or fan chart -associated with it. A weighted linear combination of the predictive densities yields a final predictive density that correctly reflects the uncertainty perceived by the agents generating the forecast. In a model-based environment, this framework allows judgement to be incorporated into fan charts in a formalised manner.JEL Classification: C15, C53, E17, E50

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Judgmental Adjustment of Statistical Forecasts

Cited by 51 publications

References 35 publications

Golden Rule of Forecasting: Be Conservative

Golden Rule of Forecasting: Be Conservative

Extrapolation for Time-Series and Cross-Sectional Data

Incorporating Judgement in Fan Charts

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