bias (systematic mistakes) or noise (unsystematic mistakes). We develop a framework for quantifying the level of noise in … structure for individual expectations. Using data from professional forecasters, we find that the magnitude of noise is large …

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 …

models describe mappings from a latent distribution to an observed distribution. The identification and estimation of … heterogeneity or unobserved state variables and panel data models with fixed effects. Recent developments in measurement error …

, and well explained by a low-dimensional latent factor structure. We then use a customized machine learning estimation …

ways that are systematic (bias) or unsystematic (noise). We provide a general method for quantifying the noise component … professional forecasters, we find that noise is large and pervasive. Our findings have implications for forecast combination, macro …