Abstract
Concerning the methodological aspects of economic prediction, the first step is to take into account the methodological scope for the kind of aim and the type of process. Chapter 11 deals with the method of prediction in economics. This requires us to consider the basis of the methodological process in economics: the kind of aim, which includes to be aware of the level of concretion of the aims as well as of the realm of the goal.
Regarding the type of process for prediction, the present analysis proposes a distinction between “predictive procedures” and “methods of prediction” in economics. In this context, several aspects are studied: (1) the characteristics of the methodological process (i.e., the preconditions for rational prediction); (2) the diversity of predictive approaches (i.e., unformalized and formalized predictions); (3) predictive procedures and predictive methods in economics; and (4) the analysis of their predictive methods by economists. Thereafter, on the role of models in economic predictions, there is an analysis of their characteristics and evaluation, followed by the study of economic modeling in the predictive realm and the presence of predictive failure with economic modeling.
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- 1.
In other cases, he distinguishes between “prediction” and “forecasting.” Then, he sees prediction as “formed from a theoretical model,” and forecasting as “limited to the extrapolations based on empirical models or data exploration” (Granger 2012, p. 312).
- 2.
The list of publications in this regard is very long. Noteworthy contributions in these thematic contexts are Granger (1990a); Pagan (1990); Hendry and Ericsson (2001a); Clements and Hendry (2002a); Newbold and Harvey (2002); Elliott et al. (2006); Clements and Hendry (2011); Elliott and Timmermann (2013a, and 2013b); and Evans (2013).
- 3.
“There are numerous ways of generating economic forecasts. Many are a mix of science—based on rigorously tested econometric systems—and judgment, occasioned by unexpected events: the future is not always like the present or the past” (Hendry and Ericsson 2001c, p. 186).
- 4.
“Although progress is being made, we are still some way from a position where the model answers can be accepted without further human intervention. This is standard international practice. McNees surveyed the large U.S. forecasting organizations in 1981; they attributed between 20 and 50 % of the final forecast to judgmental adjustments (…). Adjustments are made in the light of other information, commonsense judgements, past model error, and a knowledge of its deficiencies. The useful exercise of this judgement is not limited to the specialists. Non-specialists may also make a valuable contribution providing that the issues are put to them clearly” (Burns 1986, p. 104).
- 5.
“Knowledge of experts” is understood here in the sense of human expertise rather than an “expert knowledge” run by a computer program.
- 6.
On the distinction between “economic activity” and “economics as activity,” see Chap. 7. The historical character of economic activity is also pointed out in Sect. 8.4. The notion of “historicity” is analyzed in Gonzalez (1996e, 2011b). On the criticisms of the elbow room give to history within economic theory, cf. Hodgson (2001).
- 7.
- 8.
- 9.
Rescher’s analysis includes more elements than those which appear explicitly in the table, mainly in the area of “unformalized/judgmental” prediction. Sensu stricto, scientific predictions are—for him—predictive validation by laws and modeling.
- 10.
The Delphi procedure was developed in the 1950s by Olaf Helmer, Norman Dalkey, and Nicholas Rescher, cf. Helmer and Rescher (1959).
- 11.
According to Rescher, the difference between prediction by means of “expert system” and the informal judgment of experts lies in the process rather than in the data sources: the focus here is on the attempt to make explicit and systematize the reasoning process used by the experts themselves (a procedure used in medical diagnoses) (1998, p. 97).
- 12.
- 13.
“The accuracy of judgmental forecasts is, on average, inferior to statistical ones. This is because our judgment is often characterized by considerable biases and limitations” (Makridakis et al. 1998, p. 483).
- 14.
Consensus in error can be found in the times of Galileo Galilei and Charles Darwin.
- 15.
Fernández-Jardón, C., Personal Communication, 25 January 2014.
- 16.
- 17.
On causal order and statistical data, cf. Spirtes et al. (1993).
- 18.
“Of all predictive processes it is scientific prediction, by means of laws and law-exploiting models, that furnishes its user with the highest level of rational comfort” (Rescher 1998, p. 110).
- 19.
On the relations between representation and models in science, see Gonzalez (2014).
- 20.
The details of these predictive methods extend beyond the scope of this book, which is focused on the philosophico-methodological approach. A large amount of information can be found in the bibliography cited in this chapter and in the following one.
- 21.
Commonly, an oracle gives a “close statement”—a result—whereas a scientist offers the process followed to reach the final result.
- 22.
A different kind of statistical indicator is the diffusion index. “This provides a measure of the proportion of a given set of leading indicators that are, at any time, moving in the same direction. The pooling of indicators in the form of a diffusion index reduces the risk that false turning points in economic activity may be signalled” (Llewellyn et al. 1985, p. 127).
- 23.
On the Harvard Barometer, cf. van den Bogaard (1999, pp. 289–290).
- 24.
“Our results also indicate that there is still much to learn about forecasting the business cycle. In particular, changes in the interest rate alone do not appear to have been sufficient to forecast the onset or length of the 1990s recession, even with the benefit of hindsight. (…) It still remains to be seen how well the implied composite leading indicator will forecast any subsequent UK recession” (Osborn 2001, pp. 122–123).
- 25.
- 26.
Historically, “time-series analysts played the dominant role in developing theoretical methods for forecasting in the postwar period” (Clements and Hendry 1998, p. 9).
- 27.
It can be discussed if time series models, such as Box-Jenkins, should be considered in the group of “predictive methods” instead of being included in the set of “predictive processes.” The difference between a Box-Jenkins model and an approach of a predictive process (e.g., trend projection or curve fitting) is mostly statistical: a Box-Jenkins model is more sophisticated than a regression on a linear tendency. But it seems that, from the economic point of view, they are somehow similar. Nevertheless, the superior statistical framework leads one to think of Box Jenkins models as methodologically more sophisticated than those cases pointed out within the “predictive processes.”
- 28.
The causal models use “causal variables” as the variables that actually determine the outcome. Some authors consider that “models with no causal variables might outperform those with numerous correctly included causal variables” (Hendry and Ericsson 2001c, p. 190).
- 29.
Although there were a number of important contributions dealing with AutoRegressive Integrated Moving Average (ARIMA) class of models prior to 1970, that year marked the beginning of the popularization of this model and its associated predictive methodology, cf. Pedregal and Young (2002, pp. 71–72).
- 30.
On time series econometrics, in addition to the book of Michael Clements and David Hendry (1998), already cited, cf. Granger and Newbold (1977); Abraham and Ledolter (1983/[2005]); Harvey (1990, 1991); Kacapyr (1996a); Kennedy (1998), Chap. 17, pp. 263–287; Clements and Hendry (1999); and Hendry (2000b). See also Kock and Teräsvirta (2011), and Koopman and Ooms (2011).
- 31.
Mary Morgan introduces the distinction between “virtual experiments” and “virtually experiments”: “Virtual experiments (entirely nonmaterial in object of study and in intervention but which may involve the mimicking of observations) and virtually experiments (almost a material experiment by virtue of the virtually material object of input)” (Morgan 2003, p. 233).
- 32.
Commonly, thought experiments are used to show the possibility and the impossibility of natural phenomena, social events, and artificial designs, as well as their limits in our world. See Gonzalez (2010b, pp. 37–38).
- 33.
- 34.
Roth sees a parallel between evolutionary biology and economics because they deal “largely with historical data” (1986, p. 270).
- 35.
See, for example, Boland (1989); Rubinstein (1998); Granger (1999); and Mäki (2002), part III: Economic Models and Economic Reality, pp. 105–228. The three volumes of Models of Bounded Rationality written by Herbert Simon (1982a, 1982b, 1997) offer a good example of the many aspects interrelated in the models.
- 36.
“To evaluate an economic model, it is essential to know its purpose. Was it built to provide forecasts, to help with a policy decision, or test a specific economic hypothesis?” (Granger 2001, p. 94).
- 37.
Prima facie, the analysis has a hint paradoxical insofar as the deterministic terms are those that, in principle, have a more clear content. Underneath Hendry’s considerations can be noticed the presence of historicity of economic phenomena.
- 38.
This author offers a detailed analysis in Hendry (2000c).
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Gonzalez, W. (2015). Methods of Prediction in Economics. In: Philosophico-Methodological Analysis of Prediction and its Role in Economics. Theory and Decision Library A:, vol 50. Springer, Cham. https://doi.org/10.1007/978-3-319-08885-3_10
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