Herding and anchoring in macroeconomic forecasts: the case of the PMI
| Autor: | John B. Broughton, Bento J. Lobo |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Statistics and Probability
Economics and Econometrics 050208 finance Actuarial science Index (economics) media_common.quotation_subject education 05 social sciences social sciences Purchasing Mathematics (miscellaneous) 0502 economics and business Outlier Econometrics Economics population characteristics Herding 050207 economics Set (psychology) Private information retrieval health care economics and organizations Social Sciences (miscellaneous) Panel data Reputation media_common |
| Zdroj: | Empirical Economics. 55:1337-1355 |
| ISSN: | 1435-8921 0377-7332 |
| DOI: | 10.1007/s00181-017-1306-6 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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