Forecast performance, disagreement, and heterogeneous signal-to-noise ratios

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractWe propose an imperfect information model for the expectations of macroeconomic forecasters that explains differences in average disagreement levels across forecasters by means of cross sectional heterogeneity in the variance of private noise signals. We show that the forecaster-specific signal-to-noise ratios determine both the average individual disagreement level and an individuals' forecast performance: forecasters with very noisy signals deviate strongly from the average forecasts and report forecasts with low accuracy. We take the model to the data by empirically testing for this implied correlation. Evidence based on data from the Surveys of Professional Forecasters for the US and for the Euro Area supports the model for short-and medium-run forecasts but rejects it based on its implications for long-run forecasts.JEL classification: E37, D80

Do monetary policy transparency and central bank communication reduce interest rate disagreement?

“…8 This is an important feature of the fixed-event forecasts since the short (long)-horizon forecasts made in any month during the same calendar year for the current (next) calendar year are for the same variable. 9 As explained by Dovern and Hartmann (2017), disagreement should be measured from the fixed-horizon forecasts because the time-varying forecast horizons of the fixed-event forecasts introduce seasonal effects into the disagreement. As the survey date is closer to the target date of the fixed-event forecasts, forecasters tend to be more aligned in their expectations since they gain more information over time.…”

“…As explained by Dovern and Hartmann (), disagreement should be measured from the fixed‐horizon forecasts because the time‐varying forecast horizons of the fixed‐event forecasts introduce seasonal effects into the disagreement. As the survey date is closer to the target date of the fixed‐event forecasts, forecasters tend to be more aligned in their expectations since they gain more information over time.…”

Heterogeneous Expectations among Professional Forecasters

“…For other approaches that confront sticky and noisy information models with survey data and link them to disagreement, see, for example, Andrade and Le Bihan (2013), Dovern (2015), Andrade et al (2016), andDovern andHartmann (2017).…”

Heterogeneous Expectations among Professional Forecasters

“…For other approaches that confront sticky and noisy information models with survey data and link them to disagreement, see, for example, Andrade and Le Bihan (2013), Dovern (2015), Andrade et al (2016), andDovern andHartmann (2017).…”