Abstract
The current chapter makes an attempt to deal with the uncertain forecasts from the policy-oriented perspective of forecast users – decision makers. Its first part, Section 11.1, presents a brief introduction to the decision analysis from the Bayesian perspective. In this section, selected insights into decision making and attitudes to uncertainty are briefly discussed, together with Bayesian estimation and prediction in the decision-analytic approach. The presentation is illustrated with stylised examples concerning migration forecasts. The second part, Section 11.2, contains an overview of literature and discussion on the generic limits of predictions from the point of view of forecast users. Although the discussion is conducted in very general terms, pertaining to all types of socio-economic forecasts, its conclusion can be applied in particular to migration and population predictions. Finally, after exploring which policy questions can be answered by the forecasts, and how, an interactive approach to demographic forecasting is proposed, based on an increased role of the dialogue between forecasters and users.
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Notes
- 1.
In the general case Ω needs not to be restricted to the space of model parameters Θ and forecasted values \(\{ {\bf{x}}^{\bf{P}} \}\), but can depict any quantity of interest described using a respective probability distributions depicting uncertainty. Notation in this chapter follows DeGroot (1970/1981).
- 2.
For an argument, why, in order not to lose credibility, statisticians and demographers might prefer to avoid providing point estimates (or predictions) to the decision makers, see Alho and Spencer (2005, p. 354).
- 3.
In these cases, the formula \(q = \exp (q')\) should be used to obtain appropriate quantiles for migration rates from the ones estimated for the predictive distributions from particular forecasting models.
- 4.
For example, in R the appropriate code is v = (which.max(dt((log(x)-11)*3,10)/x) -1)*step, where x is a vector of values from the domain, from 0 to Xmax, defined with intervals of step units.
- 5.
An example of such transformation is \(T^k [L(w,d)] = \exp [k \cdot L(w,d)]\) (Adam, 2004, p. 2111).
- 6.
Such estimates or predictions are sometimes also referred to as most stable or most robust.
- 7.
Note that in the empirical examples presented in this study, the log-t predictive distributions for migration rates are heavy-tailed and their positive moments (including variance) do not exist.
- 8.
As one of the practical ways of putting ‘caps’ on payoffs or loses, Taleb (2009, p. 755) proposes insurance, although he himself admits that this strategy may not work well under very heavy tails, such as in the case of catastrophe insurance (and reinsurance).
- 9.
Recall also the argumentation of Frydman and Goldberg (2007), and Orrell (2007), presented in Chapter 10.
- 10.
For a literature review and discussion of some related issues, see for example Bijak et al. (2007).
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Bijak, J. (2011). Dealing with Uncertain Forecasts: A Policy Perspective. In: Forecasting International Migration in Europe: A Bayesian View. The Springer Series on Demographic Methods and Population Analysis, vol 24. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-8897-0_11
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