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THE PSYCHOLOGY OF ECONOMIC FORECASTING *

Published as: Wennberg, K. & Nykvist, B. (2007). The Psychology of Economic

Forecasting. Global Business and Economics Review. 9(2): 211-226.

Karl Wennberg

Center for Entrepreneurship and Business Creation

Stockholm School of Economics

P.O. Box 6501

SE-113 83 Stockholm, Sweden

Fax: +46-(0)8-318 186

E-mail: karl.wennberg@hhs.se

Björn Nykvist

Stockholm Environmental Institute

P.O. Box 2142

SE-103 14 Stockholm, Sweden

Fax: +46-(0)8-723 0348

E-mail: bjorn.nykvist@sei.se

Abstract: Is the imprecision of economic forecasts due to the judgments

of ‘biased’ decision makers? This study explores decision-making

among expert forecasters in Sweden using semi-structured interviews.

The results indicate that forecasters’ decision processes are

characterized by intuitive as well as calculating reasoning, gradually

adopting mental models and conflicting goals. While forecasters make

judgments that are non-optimal in terms of minimizing forecasting

errors, these are not necessarily biased but can be described as

ecologically rational decisions. The results indicate that behavioral

forecasting research would benefit from taking into account the specific

decision-making environment in which forecasters operate.

* We are grateful for valuable comments from Lisbeth Hedelin and two

anonymous referees on earlier versions of this paper. Remaining flaws

are entirely our own.

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INTRODUCTION

Macro economic forecasts exhibit strong influence on political and financial

decisions made by states, local authorities and private corporations alike. The general

accuracy of forecasts has however been found to be quite low, which have lead

management writers such as Mintzberg (1994) and Sherden (1998) to question the value

of economics forecasting.

Prior research on behavioral aspects of forecasting (e.g. Ash, Smyth, & Heravi,

1998; Ashley, 1988; Löffler, 1998; Pons, 2000) have tended to focus on ‘biased’

judgments, neglecting the actual motives and decision-making process taking place

among professional forecasters. In the current study we seek to challenge behavioral

forecasting research to move beyond merely determining under what circumstances

forecasters make non-optimal judgments. Drawing upon the ‘naturalistic’ strand of

judgment and decision making, we explore the decision-making process among the ten

largest forecasting institutes in Sweden. In-depth interviews with ten expert forecasters

reveal that their decision-making process is a joint process of deliberate and intuitive

thinking, characterized by gradually adopting mental models along with conflicting

motives and demands. These findings show that while forecasters make judgments that

are non-optimal in terms of minimizing forecasting errors, these are not necessarily

‘biased’ but more suitably described as ‘ecologically rational’ forecasting strategies

(Gigerenzer, Todd, & the ABC Research Group, 1999). One such strategy that we

identified was a tendency not to deviate too much from the consensus, leading to

herding behavior among forecasters. This was justified by the forecasters as the possible

benefits of ‘becoming the sole winner’ being outweighed by the risk of ‘becoming the

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sole faulty outlier’, indicating that the decision-making setting of forecasters includes

multiple, partly conflicting goals.

An implication of this study is that research on forecasters’ judgment and

decision-making need to consider the specific institutional environment in which

forecasters operate. The study also points to some avenues for future research.

Macro economic forecasts

Evaluations of GNP forecasts reveal that precision is often poor. Studies of

international GNP forecasts such as OECD’s (Ash, Smyth, & Heravi 1998; Blix et. al,

2001; Öller, & Barot, 2000; Pons, 2000), as well as growth forecasts for Sweden (Barot,

2003; Blix, Friberg, & Åkerlind, 2002; Montgomery, 2000) demonstrate the difficulties

in making accurate forecasts. Some scholars suggest that simple linear models perform

equally accurate or better than individual forecasts (Grove & Meehl, 1996; for a general

discussion see Dawes, Faust, & Meehl, 1993). In a number of cases, forecast accuracy

has proven inferior to the so-called naive forecast (Ash et al., 1998; Ashley, 1988; Öller

& Barot, 2000), i.e. the forecast equivalent to last year’s outcome.

Considerable effort has been directed to scrutinizing past forecast statistics, using

these to draw inferences about the behavior of forecasters (Ash et al., 1998; Laster,

Bennett, & Sun, 1999; Löffler, 1998) that may explain the lack of forecast accuracy.

Most of these studies have used record on past forecasts and outcomes to draw

inferences about forecaster’s behavior. From the perspective of naturalistic decision

making, this approach neglects the role of forecasting as a specific type of expertise

(Shanteau, 1995). Also, it fails to recognize the influence of the particular decision-

making setting (Klein, 1994). We therefore see a need for studies investigating

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forecasters within their own operating environment. As suggested by Montgomery

(2000), we intend to explore the decision-making process among expert forecasters

through an in-depth qualitative study. Before doing so, we provide an overview of the

forecasting industry in Sweden. We will outline the overall accuracy of forecasts from

1993 – 2001 to provide a background for the qualitative study.

This study focuses on GNP forecasts in Sweden. Despite being a relatively small

country, Sweden has several forecasting institutes such as governmental institutes,

commercial banks, as well as labor and industrial confederations. This study

concentrates on the ten largest forecasting institutes (Figure 1). These institutes present

forecasts both for the current and for the coming year. This study focuses mainly on

“one year ahead forecasts”, that is, GNP forecasts presented in the autumn for the

coming year.

--------------------------------

Insert Figure 1 about here

---------------------------------

As can bee seen in Figure 1, large deviations between forecasted and actual GNP

existed during the period 1993-2002. To briefly examine the accuracy of forecasts we

measured the Mean Errors (ME) for the ten forecasting institutes during the period. ME

measures systemic errors (i.e. whether an institute on average are overly positive or

negative) through a simple arithmetic mean of the forecast errors (Pt - At). ME errors for

the individual institutes are presented in Figure 2, the magnitude of the total mean error

for the institutes is -.20. This shows that the ten forecasting institutes had a tendency to

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underestimate GNP growth rate during the period 1994-2002, t(89) = -2.42, p < .05.

Thus forecasting institutes seem to exhibit a negative bias in their forecast decisions,

which is coherent with the findings of a contemporary Swedish study (Barot, 2003) as

well as studies of Chilean (Chumacero, 2001) and OECD forecasts (Blix, et al., 2001).

But what are the actual decisions that lie beneath such erroneous forecast judgments?

--------------------------------

Insert Figure 2 about here

---------------------------------

The apparent lack of forecast accuracy may lead to doubts over the actual value of

GNP forecast. Research on expert judgments has shown that simple linear models often

predict the future more accurately than experts do (Dawes, Faust, & Meehl, 1993;

Grove & Meehl 1996). Might there be other reasons for issuing growth forecasts than to

accurately predict future growth rates? Furthermore, are the systematic errors indicated

in Figure 2 due to deliberate strategies as suggested by Laster et al. (1999), or result

from cognitive errors as suggested by Löffler (1998)? To untangle these questions, this

study will investigate the decision-making process among expert forecasters, drawing

upon theories from the judgment and decision-making (JDM) literature.

Expert decision-making: from bias and heuristics to ecological rationality?

Research on judgment and decision-making (JDM) is a vigorous field that has

attracted the attention of several disciplines apart from cognitive psychology such as

economics, medicine, political science, marketing and management science. The field

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has to some extent been dominated by the ‘bias and heuristics’ program that emphasize

the search for cognitive mechanisms that affect people’s judgments in non-optimal ways

(Tversky & Kahneman, 1974). “Heuristics” are processes that people use in order to

design a representation of the world so as to help them to make quicker and more

efficient decisions, a ‘decision rule of thumb’, so to speak. Tversky and Kahneman

argued that these heuristics frequently lead people to systematically deviate from what

can be considered an optimal decision based on laws of probability. Although easy and

efficient, such heuristics often leads to “biases” in the sense that relevant but

contradicting information may be ignored.

Since the bias and heuristics view has received increasing interest in economics

and management science, it is interesting to note that psychologists have for a long time

debated the general validity and usefulness of the approach. A number of issues have

been raised: First, it has been argued that the bias and heuristics studies have assumed

inappropriate normative models of behavior (Birnbaum, 1983; Gigerenzer, 1991).

According to Hogarth (1981), such normative assumptions have lead to

overgeneralization of results and a general “failure to specify conditions under which

people do or do not perform well” (1981:197). Second, replicating studies has called

attention to that fact the situations presented in the most-cited studies from the bias and

heuristics program are linguistically and pragmatically unclear to subjects and

consequently not very useful in assessing individuals’ decision-making abilities

(Gigerenzer, Hell & Blank, 1988). Third, the bias and heuristics approach have

exclusively focused on groups’ central tendencies which has led to difficulties in

reconciling the variability that people display on specific tasks (Stanovich, 1999).

Fourth and possibly most relevant for economics and management science: because the

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normative tasks used in the bias and heuristics studies generally presents null

hypotheses of rational behavior as precise point estimates, it is easy to produce large-

sample directional violations in either direction (Krueger, 1998). A consequence of

these problems is that the bias and heuristics approach, albeit successfully finding a

multitude of human reasoning errors, has more seldom been able to explain why or how

human decision makers make use of heuristics (Hertwig & Todd, 2000).

A strand of the JDM literature has focused instead on the importance of studying

decision makers as they actually operate in real-world or ‘naturalistic’ settings (Klein et

al., 1993). This research has helped to improve our understanding of decision makers’

use of heuristics as ‘ecologically rational’, making adequate decisions by exploiting the

structure of information in their environment (Gigerenzer et al., 1999). Viewing

decision makers as ecologically rational would then imply that practical reasoning

mechanisms should be judged by whether or not they solve the problems they are

confronted with.

In this study we seek to investigate the decision-making process, including

reasoning, problem framing and the motives of expert forecasts. A key concern is to

investigate forecasters within their own operating environment. Orasanu and Connolly

(1993) argue that the richness, dynamism, and uncertainty of a natural context involves

a setting characterized by (1) ill-structured problems, (2) uncertain, dynamic

environments, (3) shifting, ill-defined, competing goals, (4) action–feedback loops, and

(5) high stakes associated with decisions. These characteristics correspond fairly well to

the settings of macro economic forecasting (e.g. Sherden, 1998). In this study we

therefore take a naturalistic perspective, seeking to explore and discern the vital

components of forecasters’ decision processes.

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METHOD

Since we want to provide a detailed account of the decision-making and reasoning

of expert forecasters in their own environment, we use an in-depth qualitative approach

(Klein, 1994). We expected several of the topics of interest to our study to be

controversial in the forecast community (Sherden, 1998). Personal interviews rather

than questionnaires were deemed appropriate to elicit more valid responses (Huberman

& Miles, 1994). We used a semi-structured interview guide with a fixed set of questions

but with room for elaboration and follow-ups. The questions related to (i) the decision

process leading to a forecast, (ii) perception and gathering of information, (iii)

evaluations of past forecasts, and (iv) the existence of ‘rules of thumb’ and intuitive

thinking. The full interview guide can be found in appendix A.

One expert from each of the ten largest forecasting institutes in Sweden was

interviewed. Since the goal of an exploratory study of this kind is to create a base for in-

depth and more complete understanding of a specific phenomenon (Huberman & Miles,

1994) the selection of organizations and individuals to be studied was neither accidental

nor random in statistical terms. The interviewees were chosen for “being the most

knowledgeable in forecasting” at that particular institute, i.e. a judgment based

sampling. Six of the interviewees were directors or managers officially responsible for

GNP forecasts at their forecasting institutes. The remaining four were senior forecast

experts with many years of experience in their field. This selection roughly covers the

population of larger forecasting institutes in Sweden but includes only one expert

forecaster per institute. The participants were interviewed in their own facilities by both

authors using the described semi-structured interview guide. The interviews were tape-

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recorded and lasted for approximately 90 minutes. After transcribing the interview in

full they were organized in spreadsheets according to a person*question matrix. This

approach facilitated analysis of the interview data through search for common themes

and patterns (Coffey & Atkinson, 1996). Statements and verbal nuances could thus be

refined and particular statements used to exemplify common responses were included in

the study’s results. When quantifying the number of respondents for a specific statement

we consequently use the same wordings in the text: “a few” expert should be read as

two-three experts, "several” experts as four-five experts, “most” experts as six-seven

and “almost all” experts as eight or more out of the ten respondents.

RESULTS

The result from our study is presented in order of the main results: (1) intuitive

decision-making, (2) formal and informal decision models, (3) forecast evaluation and

feedback, (4) accuracy and credibility as forecasting goals, (5) market segmentation of

economic forecasts, (6) inertia in the decision-making process. All quotations were

translated from Swedish by a professional translator.

Intuitive decision-making

The interviews clearly indicate that the expert forecasters in this study believe it is

impossible to take into account all available information when forming a forecast

judgment. One forecaster explained this as:

”It is impossible to make sense of the enormous amount of information and at the

same time consider all important aspectsin a way that is formally violable. Instead we

weigh the input on an intuitive basis.”

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Specifically, the forecasters seem to rely on certain rules of thumb (heuristics) to

identify key indicators to guide judgment of macro economic conditions. One such

heuristic that could be spotted was that all the expert forecasters interviewed seem to

have one or a few favorite “key variables”, or cues, that they observe in order to foresee

macro economic turning points. Two cues mentioned include changes in net exports and

the quarterly survey of future demand among purchasing managers. In addition, almost

all of the experts indicated that intuitive reasoning or “gut feeling” is a vital part of the

forecasting process:

”When it comes to forecasts I believe that, at the end of the day, it is gut-feeling that

determines the outcome…A comprehensive view is in fact not to be too careful or

formal. To be able to explain things intuitively is more or less what counts.”

”At all times, as with all forecasters, there is a continuous process in my head. This

means that I never begin with a blank sheet.”

The fact that a final forecast decision is to some extent based on prior beliefs and

intuition is further indicated by the answers to our question regarding how the overall

economic climate affects how expert forecasters work:

”Yes, it affects us, after all we are not robots.”

”There are, of course, people who do not want to think in these terms, as one has ones

own opinion of what these connections are like…There are those who are no suitable

for this kind of work. There are visionaries and, if I may say so, calculator-happy

people. But even if the map deviates from reality you must stick to the map.”

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When discussing various aspects of intuition and experience with the ten expert

forecasters several of them put forward an interesting description of forecasting experts

as being a close-knit community:

”There is, of course, a ‘nerdish’ element. By this I mean that a certainjargon exists

and no matter how you look at it, it all comes down to the specific knowledge and

ideas of the individual or the group. Some things are set in stone, that is for sure”

”But what you were asking is perhaps whether we get caught up in the clamor, and

that is impossible to answer, but I certainly believe this to be the case.”

Formal and informal decision models

Another part of the interview concerned the usage of formal (econometric) models

and decision tools. Most of the respondents stated that econometric models cannot be

used to fully formalize the judgmental phase of the forecasting process. Instead, smaller

models in the form as equations formalizing a relationship between different macro

economic variables are used as leading indicators, or as consistency checks to verify the

forecaster’s line of thought:

”We do not rely merely on a single model when making forecasts; instead we use

models more or less as indicators.”

”I see models more like a consistency check on my concept of the world and the rules

of thumb that I use…you weigh [information], and this you must be able to do in your

head, you cannot have your nose completely stuck in a model.”

This indicates that the decision-making process follows an iterative process,

seldom beginning with a blank sheet but with a ‘hunch’ or a more-or-less defined idea

of how economic conditions are likely to change in the time to come. Formal

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discussions, information-gathering and analyses of large amounts of data by

econometric methods are then performed jointly as the original scenario is revisited and

changed, until a final forecast is set.

Forecast evaluations and feedback

Most institutes investigated in this study do not employ any formal evaluation

procedure. A few institutes casually evaluate their GNP forecasts against actual

outcome:

”Then I must confess that we do not adhere to a strict routine, we do this only when

we have a little time to spare.”

However, a majority of the forecasters referred to some sort of informal

evaluations in their day-to-day discussions with colleagues:

”We make no formal evaluations, but you will notice whether your concept of the

world has been entirely wrong, so in a way there is feedback, although this is

completely informal.”

The lack of feedback is not surprising, given that final GNP figures are released

one and a half years after the end of the forecasting period, i.e. almost three years after a

forecast is made. During this time, preliminary outcome figures are constantly revised.

Several forecasters explained that this is a serious problem for them:

“We’re trying to hit a moving target; one could never be sure whether we missed. And

then when you get the preliminary figures you adjust to those figures that stick in your

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mind. When the outcome figures are finally released you have already moved on and

started to think of the outcome in preliminary terms. It is difficult to change your mind

like that.”

The quote above illustrates that forecasters cannot take a preliminary measured

GNP at face value, which makes effective feedback impossible. However, we found that

the three larger forecasting institutes with more resources available conducted formal

evaluation on a regular basis. The three larger forecasting institutes also have a fair

record of forecasting accuracy. If formal evaluation contributes to this accuracy, or if

forecasting accuracy as well as the existence of evaluations are both the results of

superior resources, are however questions beyond the scope of this study.

Accuracy and credibility as forecasting goals

All experts are well aware that there are several difficulties in making accurate

forecasts. Still, they point to the fact that other goals than hundred percent forecasting

accuracy are equally important:

”I do not think that authenticity is that important. I believe that it is more important to

make an intelligible forecast which explains how [macro economic] relationships

work and which cause leads to which effect, and to present a concept of the world that

is consistent.“

”So, returning to your main question, how to make accurate GNP forecasts, then

maybe the reality is that this is not our main task.”

Several of the expert forecasters considered that the most important aspect of

forecasting is to present a reliable case or scenario. To minimize forecast errors is not

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considered as important as to deliver a consistent and interesting case given a certain set

of assumptions about the future and statement about fiscal politics etc.:

I believe that the figures must be reflected in the text. The important thing is to present

a picture of what causes what. Looking at a table, you cannot really understand what

is going on. You must build a discussion outlining how things work out, and this is

what I want to put across in our forecasts.

”As I see it, there are no forecasters who make objective forecasts in the sense that

they are true mathematical expectations; instead all forecasters produce a certain

scenario.”

Several forecasters explained that what is of main concern to their users is

whether the economic situation the coming year is going to be better or worse than the

present year. Interestingly, we found a discrepancy between the expert forecasters’

interpretations of error magnitude and our own. Most of the experts seemed to consider

the errors made rather small, given the complexity of the forecasting industry:

”If you were to organize your own finances like the national accounts, then you would

know exactly how much you had spent during a certain period of time, and you would

be aware of your assets at the beginning and the end of that period…this is what we

are trying to do, but for a whole country.”

This view seemed to stem from the fact that the expert forecaster focuses on what

the actual GNP figure will be, while researchers tend to focus on GNP growth as an

annual percentage change. This is in accordance with Shanteau’s (2001) findings that

discrepancies exist between experts and researchers in how they estimate forecasting

accuracy and decisions. According to Shanteau, experts evaluate errors as if they have

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flat loss functions for deviations from optimality, whereas researchers often operate as if

they have steep loss functions. Experts consequently tend to regard small deviations as

having minor consequences, while researchers tend to regard any deviation from

optimality as having large consequences. In other words, while experts are concerned

about avoiding big mistakes, researchers are looking for perfection.

Several of the respondents mentioned that it is perfectly possible to reach an

accurate forecast by mistaking directional changes in crucial units such as net export or

private consumption if these mistakes cancel each other out. This indicates that they

evaluate themselves according to what the ecological JDM literature would describe as

‘task performance’ evaluation, as opposed to evaluating only the accuracy of a forecast:

”You should not compare GNP forecasts against actual outcome, but instead look at

any reasons for being right or wrong. It could be worse to be right in your forecast if

you say that exports will increase and private consumption decrease and it turns out

to be the other way around, even if your whole GNP forecast has proven more or less

correct.”

The market segmentation of economic forecasts

Several of the experts stated that there are clear incentives to market a forecast

scenario as to spread a certain opinion or promote their respective institute:

”Since you want to reach the customer, you must make sure your case is interesting.”

Almost all the expert forecasters said that they believe such views also exists

among some of the other institutes. This indicates the existence of strategic biases, a

deviation from error-minimizing judgment due to some sort of conscious strategic

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behavior. Forecasters have incentives to deviate from the consensus due to competing

goals which might be difficult to reconcile: to minimize forecast errors and to obtain

publicity. These findings have been documented in prior research on forecasters in other

countries (Batchelor & Dua, 1990; Laster et al., 1999; Löffler, 1998) and could be

explained by classical theories how economic agents will respond to conflicting goals

(Jensen & Meckling, 1976). Users of forecasts might demand maximized forecast

accuracy, but since forecasting institutes in part subsist on the amount of publicity they

generate, expert forecasters try to reach a compromise between these two goals. In other

words, they have motives that sometimes lead them to deviate from what can be

considered an optimal forecast. There also seems to be a time aspect involved. Several

forecasters interviewed in this study stated that forecasts released at a later date would

probably have a higher likelihood of deviation from forecasts released by other

institutes at an earlier date:

”There is a need for profiling. Say, for example, that all [forecast institutes] have

negatively revised [their forecasts]. Then you would want to profile your forecast in a

new way that is interesting and makes the headlines. It is as if there is a segment in

the forecasting market that has not been claimed, and therefore it is tempting to claim

that segment. And if you think it’s not so much of a gamble, then you alone have the

chance of being in the right.”

This “marketing segmentation of forecasts” has been explained by Bachelor and

Dua (1990) as a way for forecasters to target a group of forecast users who fit a certain

psychographic mind-set: “Economic forecasters have therefore differentiated their

products by adopting what we labeled an ´extremist´ strategy” (p. 313). A few of the

forecasters interviewed in the current study also mentioned that a forecaster with a

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generally negative or positive economic outlook might be inclined to seek employment

at a forecasting institute renowned for its generally negative or positive forecasts.

However, none of the forecasters interviewed in this study agreed with the statement

that forecasters deviate too much from the consensus. Several interviewees indicated

that they as expert forecasters generally are too dependent on each other’s forecasts.

Most of the forecasters also expressed a desire to make bolder predictions:

”As a forecaster I would say that it is worse to be the only one in the wrong than

gainful to be the only one in the right, and in so beating all the others. Thus there is

an incentive to be careful not to deviate from the norm, and so one might be afraid of

free thinking.”

These quotes indicate that expert forecasters as a profession may have asymmetric

loss function for presenting errors. This would explain the existence of herding

behavior, which has been indicated in earlier studies of Swedish (Barot, 2003; Blix et

al., 2002) and international (Batchelor & Dua, 1991; Blix et al., 2001; Pons-Novell,

2003) forecasters.

Inertia in the decision-making process

The expert forecasters in this study seem to base their decisions on a mixture of

previous beliefs and formal modeling of the economic statistics. Most forecasters also

state that their main goal is to present a reliable ‘case’. In the JDM literature this is

referred to as ‘mental models’ (Klein, 1994). Mental models lets experts know how a

complex decision is dependent on various sub-decisions that they can perform more or

less automatically, without having to break down the decision into separate tasks. The

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concept of forecasting cases as mental models is a key for understanding the decision-

making processes of expert forecasters: The mental models, refereed to by the

forecasters as cases, somewhat reluctantly have to be redefined as new information

arrives:

”Also, you have constructed a story for yourself that you believe in, and you are not

easily persuaded that this story might be utterly wrong. Unfortunately I believe that

this is a human trait.”

“I also believe that there is some prestige in sticking to your original point of view.”

The case constitutes a continuously changing model of the current situation and

when new information arrives this model has to be adjusted according to the new

situation, which is not easily done. Instead, the expert forecasters seem prone to stick to

their case until this is proven false, introducing an inertia in the decision-making

process. The interviews however indicate that this inertial process might also have a

positive feature: Cooperation and consensus seeking among expert forecasters is more

common among the larger forecasting institutes. Here, individual’s inertia may

contribute to better forecasts for larger institutes where more forecasters have to

exchange opinions:

”I believe that a small institute has 1 to 2 key people. This means that any forecast

will be capricious in the sense that these two individuals’ ideas will be decisive.

Naturally two people at different institutes may interpret information in different

ways, or put emphasis on different things. But if there are fifty people who must agree

on a conclusion then you are bound to end up with a more concordant solution.”

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”Eventually some sort of consensus will form that is the result of some sort of

compromise…we are 20-30 people here so if any form of consensus opinion is to be

changed this will be done with a certain inertia.”

DISCUSSION AND CONCLUSIONS

In this study we have put forward that studying expert forecasters in their natural

decision-making situation provides a better understanding of the decision-making

process behind economic forecasts. Our findings point at some avenues for further

research regarding the goals, decision processes, and performance of economic

forecasters.

First, we found evidence of intuitive thinking and the use of formal models being

joint components in forecast judgments, used in an iterative process together with expert

knowledge. That intuition and gut feeling are important elements of forecasting can be

explained by Bechara and Damasio’s (2005) Somatic market hypothesis. With the

complexity and unclear patterns of a rapid market economy, cognition struggles

explicitly with figuring out a ’best’ strategy, while somatic signals help select the most

advantageous response option (Bechara & Damasio, 2005).

Second, we found indications that forecasters’ primary objective might not be to

minimize forecast error but to construct a consistent and credible ‘case’. Also,

subjective mental models in the form of forecast cases play an important role in

explaining inertia in the decision-making process when presented to contrasting

information. These mental models to some extent build on the accumulated and implicit

expert knowledge of forecasters.

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That heuristics, or cues, are used in order to foresee macro economic turning

points is also consistent with other JDM studies on financial decision making among

auditors (Shanteau, 1995) and professional stockbrokers (Slovic, 1969). Given the

exploratory nature of the study, these and other results should be seen as tentative,

indicating interesting hypothesis to be examined in future work. For example, we do not

know enough about the specific heuristics used by forecasters to determine the relative

usefulness of these compared to other types of forecasting tools such as formal

econometric models.

Our study also indicates herding behavior as a likely explanation for earlier

findings that strategic biases affect forecasters’ decision-making (Laster et. al, 1998).

However, more research is needed to determine how the effects of herding behavior and

marketing segmentation interact in shaping forecasts judgments.

In addition, expert forecasters describe their decision-making process as “too

rigid” due to mental models and ideas built around the specific scenario used in a

forecast. Similarly, Schwartz (1987) found that economic decision makers in Latin

America residing in countries with little experience of devaluation were more prone to

stick to a steady forecast long after there were clear signs of devaluation, compared to

decision makers residing in countries where devaluation was more frequent. The

forecasters in this study seem prone to believe at length in a particular case until this is

falsified, leading to a rigid decision-making process. Future research might capitalize on

these findings and investigate whether this rigidity may explain forecasters’ inability to

predict macro economic turning points.

On a more general level, the findings from this study provide an argument that

forecasters should be viewed as ‘ecological rational’ rather than ‘biased’ decision

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makers. Such a view would shed new light on prior research that has pointed to the

inaccuracy of economic forecasting. The chief implication of this study is therefore that

research on forecaster’s judgment and decision-making need to consider the specific

institutional environment in which forecasters operate.

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Figure 1. GNP forecasts and outcomes at t + 1. Data source: Statistics Sweden.

Figure 2. Mean Errors (ME) for GNP t + 1 forecasts during 1994 - 2002.

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Appendix A: Interview Guide

Decision process

Can you describe your work process when you are working towards a forecast?

-collectively?

-individually?

For whom do you publish forecasts?

Who are your customers?

How do you cooperate with them?

Information, perception, and judgmental tools

Which types of information and econometric models do you use?

How reliable do you consider this information to be?

In which ways do you use this information in the econometric models you use?

Which kind of information means most to determine the outcome of econometric models?

Perception

How do you sift through between different kinds of information?

Do you discuss how information can be interpreted in different ways?

Can you describe these discussions?

How does the general future outlook and economic climate affect the assumptions that you take in a

specific forecast? [example: now – insecurity, 1999 – boom, 1993 – bust]

Judgment

How are the routines regarding the analysis preceding a forecast decision?

When/how do you change your routines? [ex. new staff]

Would you say that there is some sort of “ideal” decision process?

How does this process look like?

Do you strive for such a process?

In what way?

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Intuitive decision making

When you enter numbers into a formal model, do you have ”a hunch” of how the result of the model will

turn out?

How would you describe the concept of ”intuition”?

Is it relevant for your forecasting?

Is it based on prior experiences? How?

Do you discuss these types of questions? How?

Decision

Who takes the final decision in calling a forecast ’ready’?

Who has professional responsibility for the forecasts that you issue?

If there are question marks or internal conflicts regarding a question that is vital for the forecast, how do

you handle this?

What is your view on the difficulties to present a reliable forecast at the same time as uncertainty is high?

What is your view on the concept of “a naive forecast”? [ A. Equal to t-1., or B: Different types of

moving averages of earlier forecasts”]

Evaluation

Do you compare the outcomes of your forecasts with that of other institutes?

Why are the forecasts so different between institutes when you all have access to almost the same data?

If a forecasts turn out to be far from the mark, what is generally the reasons?

How do you evaluate earlier forecasts?

Would it be possible to improve your decision making process in any way?

Overall process

What happens after the forecast is issued?