Abstract
The forecast histograms reported by the respondents to some surveys, such as the US SPF, are typically viewed as estimates of the individuals’ subjective distributions. There are various ways of calculating the quantities of interest from the histograms, and these are reviewed. Quantities of interest might include first moments such as means and modes, as well as variances, and the forecast probability that Y will be less than some value. The last is typically required by popular methods of evaluating the histograms. The difficulties arise because the histograms do not fully reveal the respondents’ probability density/distribution function, and the methods include non-parametric techniques, as well as the fitting of parametric distributions, including distributions which allow for asymmetry in the individual’s underlying assessments.
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- 1.
The location of the bins (and so the x’s) and their number and widths (w) vary over the sample period, but this is suppressed in the notation for convenience.
- 2.
We assume that the interval 2 to 2.9 is actually [2,3], so that the interval is of length 1. This means that \(F\left ( 3\right ) =0.2\), for example (as opposed to \(F\left ( 2.9\right ) =0.2\)). This assumption is implicit in how we have written the fourth column of Table 3.1, and underpins the fitting of the parametric distributions.
- 3.
See the online documentation provided by the Philadelphia Feb: ‘Documentation for the Philadelphia Fed’s Survey of Professional Forecasters’, http://www.phil.frb.org/econ/spf/. The problematic survey quarters are 1985.1, 1986.1, 1968.4, 1969.4, 1970.4, 1971.4, 1972.3, 1972.4, 1973.4, 1975.4, 1976.4, 1977.4, 1978.4, 1979.2, 1979.3, 1979.4.
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Clements, M.P. (2019). Working with the Forecast Data. In: Macroeconomic Survey Expectations. Palgrave Texts in Econometrics. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-97223-7_3
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