Judgmental forecasting: A review of progress over the last 25 years

By Michael Lawrence, Paul Goodwin, Marcus O'Connor, D. Onkal. 2006

532 citations recorded by the source corpus.

Subfields: General Decision Sciences, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Forecasting at Scale (2018, neutral): “This section is agnostic to the forecasting method used by the analyst and contains some best-practices we have settled on while shipping production business forecasts across a variety of applications.”
Correcting Forecasts with Multifactor Neural Attention (2016, background): “One such issue is that humans have biases when analyzing the impact of external data sources (Lawrence et al., 2006).”
Forecasting and operational research: a review (2008, background): “Lawrence et al (2006) survey the many issues that are involved in incorporating judgement effectively.”
The credit-to-GDP gap and countercyclical capital buffers: questions and answers (2014, background): “Forecasts and signals that policymakers find hard to understand and interpret are likely to be ignored (eg Önkal et al (2002), Lawrence et al (2006)).”
Lancaster University Management School Working Paper 2007 / 005 Against your better judgment ? How organizations can improve their use of management judgment in forecasting (2007, unknown): “Most researchers agree that management judgment can play a valuable role in forecasting (Lawrence et al. 2006).”
Evaluating early warning indicators of banking crises: Satisfying policy requirements (2014, background): “The final, less tangible, requirement is that EWI signals should be easy to interpret, as any forecasts, including EWIs, that do not “make sense” are likely to be ignored by policy makers (eg Önkal et al (2002), Lawrence et al (2006)).”
Statistical tests for multiple forecast comparison (2012, background): “…when financial institutions provide forecasts without disclosing the models that generated the forecasts or in judgmental forecasting where the forecaster may use non-time series information to improve the forecasts (see Lawrence et al. 2006 for a review of judgmental forecasting techniques).”