Forecasting with Conjoint Analysis

Wittink, Dick R.; Bergestuen, Trond

doi:10.1007/978-0-306-47630-3_8

Cited by 39 publications

(22 citation statements)

References 24 publications

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“…Although conjoint analysis seems to be based on solid principles, there have been no tests against alternative methods, despite repeated calls for such research (Wittink & Bergestuen 2001).…”

Section: Conjoint Analysismentioning

confidence: 99%

Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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Empirical comparisons of reasonable approaches provide evidence on the best forecasting procedures to use under given conditions. Based on this evidence, I summarize the progress made over the past quarter century with respect to methods for reducing forecasting error. Seven well-established methods have been shown to improve accuracy: combining forecasts and Delphi help for all types of data; causal modeling, judgmental bootstrapping and structured judgment help with cross-sectional data; and causal models and trend-damping help with time-series data. Promising methods for cross-sectional data include damped causality, simulated interaction, structured analogies, and judgmental decomposition; for time-series data, they include segmentation, rule-based forecasting, damped seasonality, decomposition by causal forces, damped trend with analogous data, and damped seasonality. The testing of multiple hypotheses has also revealed methods where gains are limited: these include data mining, neural nets, and Box-Jenkins methods. Multiple hypotheses tests should be conducted on widely used but relatively untested methods such as prediction markets, conjoint analysis, diffusion models, and game theory. KeywordsBox-Jenkins, causal forces, causal models, combining forecasts, complex series, conjoint analysis, contrary series, damped seasonality, damped trend, data mining, Delphi, diffusion, game theory, judgmental decomposition, multiple hypotheses, neural nets, prediction markets, rule-based forecasting, segmentation, simulated interaction, structured analogies AbstractEmpirical comparisons of reasonable approaches provide evidence on the best forecasting

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Section: Conjoint Analysismentioning

confidence: 99%

Findings from evidence-based forecasting: Methods for reducing forecast error

Armstrong

2006

International Journal of Forecasting

151

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“…One restriction is that these cases should all seem realistic to the experts. The design procedures are similar to those used for conjoint analysis (Wittink and Bergestuen 2001).…”

Section: • Use the Most Successful Expertsmentioning

confidence: 99%

Judgmental Bootstrapping: Inferring Experts’ Rules for Forecasting

Armstrong

2001

International Series in Operations Research &Amp; Management Science

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Judgmental bootstrapping is a type of expert system. It translates an experts' rules into a quantitative model by regressing the experts' forecasts against the information that he used. Bootstrapping models apply an experts' rules consistently, and many studies have shown that decisions and predictions from bootstrapping models are similar to those from the experts. Three studies showed that bootstrapping improved the quality of production decisions in companies. To date, research on forecasting with judgmental bootstrapping has been restricted primarily to cross-sectional data, not time-series data. Studies from psychology, education, personnel, marketing, and finance, showed that bootstrapping forecasts were more accurate than forecasts made by experts using unaided judgment. They were more accurate for eight of eleven comparisons, less accurate in one, and there were two ties. The gains in accuracy were generally substantial. Bootstrapping can be useful when historical data on the variable to be forecast are lacking or of poor quality; otherwise, econometric models should be used. Bootstrapping is most appropriate for complex situations, where judgments are unreliable, and where experts' judgments have some validity. When many forecasts are needed, bootstrapping is cost-effective. If experts differ greatly in expertise, bootstrapping can allow one to draw upon the forecasts made by the best experts. Bootstrapping aids learning; it can help to identify biases in the way experts make predictions, and it can reveal how the best experts make predictions. Finally, judgmental bootstrapping offers the possibility of conducting ?experiments@ when the historical data for causal variables have not varied over time. Thus, it can serve as a supplement for econometric models. ABSTRACTJudgmental bootstrapping is a type of expert system. It translates an expert=s rules into a quantitative model by regressing the expert=s forecasts against the information that he used. Bootstrapping models apply an expert=s rules consistently, and many studies have shown that decisions and predictions from bootstrapping models are similar to those from the experts. Three studies showed that bootstrapping improved the quality of production decisions in companies. To date, research on forecasting with judgmental bootstrapping has been restricted primarily to cross-sectional data, not time-series data. Studies from psychology, education, personnel, marketing, and finance, showed that bootstrapping forecasts were more accurate than forecasts made by experts using unaided judgment. They were more accurate for eight of eleven comparisons, less accurate in one, and there were two ties. The gains in accuracy were generally substantial. Bootstrapping can be useful when historical data on the variable to be forecast are lacking or of poor quality; otherwise, econometric models should be used. Bootstrapping is most appropriate for complex situations, where judgments are unreliable, and where experts= judgments have some validity. When many for...

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“…(These simulated purchase environments are often web-based.) However, we omit a fuller discussion of these models because, as Wittink and Bergestuen (2001) point out, they are seldom validated within a forecasting context, despite this being their ultimate purpose.…”

Section: New-product Modelsmentioning

confidence: 99%

Forecasting and operational research: a review

Fildes

Νικολόπουλος

Crone

et al. 2008

Journal of the Operational Research Society

227

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From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.

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Forecasting with Conjoint Analysis

Cited by 39 publications

References 24 publications

Findings from evidence-based forecasting: Methods for reducing forecast error

Findings from evidence-based forecasting: Methods for reducing forecast error

Judgmental Bootstrapping: Inferring Experts’ Rules for Forecasting

Forecasting and operational research: a review

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