Abstract
Forecasts from surveys are assumed to reflect probabilistic beliefs. This interpretation is reinforced by the reporting of probability distributions alongside the point forecasts. Sources of survey expectations are discussed, as are the nature of the forecasts: fixed-event and rolling-event forecasts. Surveys such as the US Survey of Professional Forecasters (SPF) report the responses of the individual forecasters, although these are often aggregated for subsequent analyses, and attention focuses on the aggregate or consensus forecast. Problems of interpreting temporal variation in the aggregate forecast are discussed, particularly when there is entry and exit, or occasional non-response by active participants, resulting in changing composition of the panel. When individuals possess private information, there may be better ways of aggregating forecasts than traditional techniques, such as taking the mean or median.
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Knight goes on to state that ‘Even in these extreme cases, however, there is a certain vague grouping of cases on the basis of intuition or judgment; only in this way can we imagine any estimate of a probability being arrived at.’
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Pesaran (1987) provides a critique of rational expectations.
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The complete survey data and documentation are available at https://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters/. This website provides sample survey questionnaires, and an academic bibliography of articles that use the survey: see https://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters/academic-bibliography.
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According to Boero et al. (2015), access to the survey data can be gained by writing to the Publications Editor, Inflation Report and Bulletin Division, Bank of England, Threadneedle Street, London EC2R 8AH, UK.
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The contrast being made here is to model forecasts. Retrospective model forecasts can be made of any variable, horizon, or target, at any frequency, limited only by the skill and ingenuity of the researcher. A common requirement for comparison to survey forecasts is that the model forecasts are ‘real time’, in the sense that forecasts are made from models specified and estimated using data that would have been available at that specific time, that is, data vintages that the survey forecaster would have had access to (e.g., Croushore 2006, 2011). The real-time forecasting literature generally seems less concerned whether the forecasting technology (models, methods, and computational devices) would also have been known. Model forecasts are obviously of no value in terms of behavioural explanations of how expectations are actually made.
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Capistrán and Timmermann (2009) suggest that often a simple bias adjustment to the equal-weighted combination might be required.
- 9.
We are assuming the forecaster is targeting the (conditional) mean of the distribution of y. The class of loss functions (or scoring rules) which are ‘proper’ or ‘consistent’ for the mean includes squared-error loss but also all members of the Bregman class. By ‘proper for the mean’ is meant that a forecaster faced with squared-error loss (or a Bregman loss function more generally) will have no incentive not to report her conditional mean. See, for example, Gneiting (2011). In the absence of information to the contrary, we generally assume that survey point predictions are conditional expectations and that the appropriate way of evaluating such forecasts is by squared-error loss. Patton (2017) argues that if forecasts are mis-specified (in a sense in which he explains), simply assuming a proper scoring function is not enough, because the ranking of such forecasts may be sensitive to the specific member of the Bregman class used for forecast evaluation (in the case of conditional mean forecasts). In principle, the forecaster ought to optimize her (mis-specified) conditional mean forecast for the single member of the Bregman class of loss functions to be used to evaluate it.
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Interestingly, Clements (2018) shows that whether forecasters disagree because of noise or private information can be an important determinant of the outcome of putative tests of herding.
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Carroll (2003) explicitly models households’ expectations as responding with a lag to the expectations of professional forecasters.
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Clements, M.P. (2019). The Nature of Survey Expectations. In: Macroeconomic Survey Expectations. Palgrave Texts in Econometrics. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-97223-7_2
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