Herding and anchoring in macroeconomic forecasts: the case of the PMI

• Published in: Empirical economics Vol. 55; no. 3; pp. 1337 - 1355
• Main Authors: Broughton, John B., Lobo, Bento J.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2018; Springer Nature B.V
• Subjects: Anchoring; Bias; Clustering; Econometrics; Economic forecasting; Economic models; Economic theory; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Economics and Finance; Finance; Forecasting; Incentives; Insurance; Longitudinal studies; Macroeconomics; Management; Purchasing managers index; Statistics for Business; Panel data; E44; (Anti)-herding; G24; Multiple biases; (Anti)-anchoring; Forecast accuracy; G17; PMI; D83; C83
• ISSN: 0377-7332; 1435-8921
• DOI: 10.1007/s00181-017-1306-6

We test whether analysts display multiple biases in forecasting the Institute for Supply Management’s manufacturing purchasing manager’s index (PMI). We adopt a test that does not require knowledge of the forecaster’s prior information set and is robust to rational clustering, correlated forecast errors and outliers. We find that analysts forecast the PMI poorly and display multiple biases when forecasting. In particular, forecasters anti-herd and anti-anchor. Anti-herding supports a reputation-based notion that forecasters are rewarded not only for forecast accuracy but also for being the best forecast at a single point in time. Anti-anchoring is consistent with forecasters overreacting to private information. The two biases show a strong positive correlation suggesting that the incentives that elicit anti-herding also elicit anti-anchoring behavior. Both biases result in larger absolute errors, although the effect is stronger for anti-herding.