Comparing the Point Predictions and Subjective Probability Distributions of Professional Forecasters

By Joseph E. Engelberg, Charles F. Manski, Jared C. Williams. 2009

378 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/d5e2f6e8d16c19ef118baf5d41d590138c3c52d6

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In Economics and Econometrics

Citations in the corpus

Making and Evaluating Point Forecasts (2009, background): “In this light, it may be desirable for administrative or political leadership to provide a directive or target functional to Census Bureau scientists, much in the way that Murphy and Daan (1985) and Engelberg et al. (2009) requested guidance for point forecasters, in the quotes that open and motivat”
Survey Measurement of Probabilistic Macroeconomic Expectations: Progress and Promise (2017, neutral): “3 To shed empirical light on the reporting practices of professional forecasters, Engelberg et al. (2009) used data from the SPF to compare point predictions of GDP growth and inflation with the subjective probability distributions held by forecasters.”
Measuring Forecast Uncertainty by Disagreement: The Missing Link (2008, methodology): “Following Zarnowitz and Lambros (1987), we measure overall forecast uncertainty (Uth) as the average of the individual forecast error variances Uth 1N N∑ iD1 Uith, which can be interpreted as the confidence an outside observer will have in a randomly drawn typical individual forecast from the panel ”
Higher order elicitability and Osband’s principle (2015, background): “Gneiting (2011) demonstrated impressively that scoring functions should be incentive compatible in that they should encourage the forecasters to issue truthful reports; see also Murphy and Daan (1985); Engelberg et al.”
Measuring Subjective Expectations in Developing Countries: A Critical Review and New Evidence (2009, background): “The 8 Engelberg et al. (2009) compare point predictions of GDP growth and inflation”
The Formation of Expectations, Inflation, and the Phillips Curve (2018, background): “For example, point predictions may be rounded to the nearest “five” (Binder, 2015) or be more optimistic than the subjective probability distribution would imply (Engelberg et al., 2009).”
Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings (2015, methodology): “In Section 3, we apply the mixture representations to study forecast rankings and propose the aforementioned Murphy diagram for forecast comparisons.”
Uncertainty and disagreement in economic prediction : the Bank of England Survey of External Forecasters (2008, neutral): “For the US SPF data, 1992–2004, Engelberg et al. (2008) document the extent to which reported point forecasts deviate from calculated measures of central tendency (mean/median/mode).”
Comparing Possibly Misspecified Forecasts (2020, positive): “If the loss functions used are not consistent for the same statistical functional, then it is not surprising that the rankings may differ across loss functions (see, e.g., Engelberg, Manski, and Williams 2009; Gneiting 2011a; Patton 2011).”
Forecasting: theory and practice (2020, background): “Engelberg et al. (2009) and Clements (2009, 2010) considered the consistency between the point predictions and histogram forecasts.”
The Price Is Right: Updating Inflation Expectations in a Randomized Price Information Experiment (2016, methodology): “…an ex post realized outcome as a benchmark for accuracy of ex ante expectations, since inflation outcomes are uncertain and respondents’ point forecasts may refer to various statistics (i.e., mean, median, mode, or others) of their subjective probability distributions (Engelberg et al., 2009).”