This paper provides one of the first comprehensive analyses of the household data underlying the Michigan Index of Consumer Sentiment. This data is used to test the rationality of consumer expectations and to assess their usefulness in forecasting expenditure. The results can also be interpreted...

Policymakers, firms, and investors closely monitor traditional survey-based consumer confidence indicators and treat it as an important piece of economic information. We propose a latent factor model for the vector of monthly survey-based consumer confidence and daily sentiment embedded in...

We consider the problem of short-term time series forecasting (nowcasting) when there are more possible predictors than observations. Our approach combines three Bayesian techniques: Kalman filtering, spike-and-slab regression, and model averaging. We illustrate this approach using search engine...

Social media data presents challenges for forecasters since one must convert text into data and deal with issues related to these measures being collected at different frequencies and volumes than traditional financial data. In this paper, we use a deep learning algorithm to measure sentiment...

Policymakers, firms, and investors closely monitor traditional survey-based consumer confidence indicators and treat it as an important piece of economic information. We propose a latent factor model for the vector of monthly survey-based consumer confidence and daily sentiment embedded in...

This paper provides one of the first comprehensive analyses of the household data underlying the Michigan Index of Consumer Sentiment. This data is used to test the rationality of consumer expectations and to assess their usefulness in forecasting expenditure. The results can also be interpreted...