Irrationality or efficiency of macroeconomic survey forecasts? Implications from the anchoring bias test

We analyze the quality of macroeconomic survey forecasts. Recent findings indicate that they are anchoring biased. This irrationality would challenge the results of a wide range of empirical studies, e.g., in asset pricing, volatility clustering or market liquidity, which rely on survey data to capture market participants' expectations. We contribute to the existing literature in two ways. First, we show that the cognitive bias is a statistical artifact. Despite highly significant anchoring coefficients a bias adjustment does not improve forecasts' quality. To explain this counterintuitive result we take a closer look at macroeconomic analysts' information processing abilities. We find that analysts benefit from the use of an extensive information set, neglected in the anchoring bias test. Exactly this information advantage drives the misleading anchoring bias test results. Second, we find that the superior information aggregation capabilities enable analysts to easily outperform sophisticated timeseries forecasts and therefore survey forecasts should clearly be favored. --macroeconomic announcements,efficiency of forecasts,anchoring bias,rationality of analysts

Should one rely on professional exchange rate forecasts: An empirical analysis of professional forecasts for the /US-$ rate

The study analyses the characteristics of professional exchange rate forecasts for the /US-$ rate. The results indicate that the quality of forecasts produced by profes-sional economists is rather poor and incompatible with the rational expectations hy-pothesis. This dismal result is according to our analysis attributed to the fact that professional forecasts are to a large extend influenced by actual changes in exchange rates. A reasonable explanation for this behaviour can be derived from the behav-ioural finance literature. According to the anchoring heuristic decision processes are often dominated by available pieces of information even if they are obviously of no relevance. --foreign exchange market,rational expectations,forecasts,behavioural finance,anchoring heuristics

Contingent Valuation of Community Forestry Programs in Ethiopia: Observing Preference Anomalies in Double-Bounded CVM

This study examines the potential for anomalous response behaviour effects within the context of double-bounded contingent valuation methods applied to community forestry programs in rural Ethiopia. Anomalous responses considered include shift effects, framing effects and anchoring effects, and these effects are considered within a double-bounded contingent valuation study. The results confirmed the presence of incentive incompatibility and framing effects. However, anchoring effects are not uncovered. After controlling for these biases, the community forestry program considered is shown to offer a welfare gain ranging from Ethiopian Birr (ETB) 20.14 to 22.80. In addition to these welfare benefits, the results raise questions with respect to the validity of previous welfare estimates associated with double-bounded CVM studies in developing countries, suggesting that future studies should control for incentive incompatibility and framing effects bias.Double-bounded CVM, incentive incompatibility bias, anchoring bias

Complexity, Evolution and Learning: a simple story of heterogeneous expectations and some empirical and experimental validation.

This note discusses complexity models in economics. A key feature of these models is that agents have heterogeneous expectations, disciplined by adaptive learning and evolutionary selection. Agents adapt their rules based upon past observations and switch between different forecasting heuristics based upon strategy performance. We discuss how these models match empirical facts as well as laboratory experiments with human subjects and how this approach may tame the ``wilderness of bounded rationality''.

Predicting with sparse data

It is well known that effective prediction of project cost related factors is an important aspect of software engineering. Unfortunately, despite extensive research over more than 30 years, this remains a significant problem for many practitioners. A major obstacle is the absence of reliable and systematic historic data, yet this is a sine qua non for almost all proposed methods: statistical, machine learning or calibration of existing models. In this paper we describe our sparse data method (SDM) based upon a pairwise comparison technique and Saaty's Analytic Hierarchy Process (AHP). Our minimum data requirement is a single known point. The technique is supported by a software tool known as DataSalvage. We show, for data from two companies, how our approach — based upon expert judgement — adds value to expert judgement by producing significantly more accurate and less biased results. A sensitivity analysis shows that our approach is robust to pairwise comparison errors. We then describe the results of a small usability trial with a practising project manager. From this empirical work we conclude that the technique is promising and may help overcome some of the present barriers to effective project prediction

Psychophysics and the judgment of price: judging complex objects on a non-physical dimension elicits sequential effects like those in perceptual tasks

When participants in psychophysical experiments are asked to estimate or identify stimuli which differ on a single physical dimension, their judgments are influenced by the local experimental context — the item presented and judgment made on the previous trial. It has been suggested that similar sequential effects occur in more naturalistic, real-world judgments. In three experiments we asked participants to judge the prices of a sequence of items. In Experiment 1, judgments were biased towards the previous response (assimilation) but away from the true value of the previous item (contrast), a pattern which matches that found in psychophysical research. In Experiments 2A and 2B, we manipulated the provision of feedback and the expertise of the participants, and found that feedback reduced the effect of the previous judgment and shifted the effect of the previous item’s true price from contrast to assimilation. Finally, in all three experiments we found that judgments were biased towards the centre of the range, a phenomenon known as the “regression effect” in psychophysics. These results suggest that the most recently-presented item is a point of reference for the current judgment. The findings inform our understanding of the judgment process, constrain the explanations for local context effects put forward by psychophysicists, and carry practical importance for real-world situations in which contextual bias may degrade the accuracy of judgments

Biases of professional exchange rate forecasts: Psychological explanations and an experimentally based comparison to novices

The empirical performance of macroeconomic exchange rate models is more than disappointing. This dismal result is also reflected in the forecasting capabilities of professional analysts: all in all, analysts are not in a position to beat naïve random walk forecasts. The root for this deficient outcome stems from the fact that professional forecasts are to a large extend influenced by actual changes in exchange rates. A reasonable explanation for this behaviour can be taken from the behavioural finance literature. To test whether this characteristic tends to be general human behaviour in an uncertain environment, we analyse the forecasting behaviour of students experimentally, using a simulated currency series. Our results indicate that a topically oriented trend adjustment behaviour (TOTA) is a general characteristic of human forecasting behaviour. Additionally, we apply a simple model to explain professional and students forecasts. --Foreign exchange market,forecasting,behavioural finance,anchoring heuristics,judgement,expertise

Knowledge of previous tasks: task similarity influences bias in task duration predictions

Bias in predictions of task duration has been attributed to misremembering previous task duration and using previous task duration as a basis for predictions. This research sought to further examine how previous task information affects prediction bias by manipulating task similarity and assessing the role of previous task duration feedback. Task similarity was examined through participants performing two tasks 1 week apart that were the same or different. Duration feedback was provided to all participants (Experiment 1), its recall was manipulated (Experiment 2), and its provision was manipulated (Experiment 3). In all experiments, task similarity influenced bias on the second task, with predictions being less biased when the first task was the same task. However, duration feedback did not influence bias. The findings highlight the pivotal role of knowledge about previous tasks in task duration prediction and are discussed in relation to the theoretical accounts of task duration prediction bias