') bias in the estimates and, after due account is taken of this bias, we find that differences due to estimation method are …

reliance on intuitions. In none of the tasks are very high stakes sufficient to de-bias participants, or come even close to …

This paper quantifies the amount of noise and bias in analysts' forecast of corporate earnings at various horizons. We … next decompose the relative accuracy of these forecasts into three components: (i) noise, (ii) bias and (iii) analysts … both noise and bias are increase linearly. We then show most existing models lack a mechanism to account for these facts …

Platforms are growing increasingly powerful, raising questions about whether their power might be exercised with bias …. While bias is inherently difficult to measure, we identify a context within the music industry that is amenable to bias … suggest the possibility of platform biases in favor of major record labels, and industry participants also point to bias …

the spot rate. We use the surveys to decompose the bias into a protion attributable to the risk premium and a portion … attributable to systematic prediction errors. The survey data suggest that our findings of both unconditional and conditional bias …

In this paper, I present a simple characterization of the sample selection bias problem that is also applicable to the … problem of sample selection bias is fit within the conventional specification error framework of Griliches and Theil. A simple … estimator is discussed that enables analysts to utilize ordinary regression methods to estimate models free of selection bias …

We model visibility bias in the social transmission of consumption behavior. When consumption is more salient than non …