The coronavirus is a global event of historical proportions and just a few months changed the time series properties of the data in ways that make many pre-covid forecasting models inadequate. It also creates a new problem for estimation of economic factors and dynamic causal effects because the...

This paper analyzes weekly scanner data collected for 108 groups at the county level between 2006 and 2014. The data display multi-dimensional weekly seasonal effects that are not exactly periodic but are cross-sectionally dependent. Existing univariate procedures are imperfect and yield...

Beliefs are important determinants of an individual's choices and economic outcomes, so understanding how they differ across individuals is of considerable interest. Researchers often rely on surveys that report individual expectations as qualitative data. We propose using a Bayesian...

The outbreak of COVID-19 has significantly disrupted the economy. This paper attempts to quantify the macroeconomic impact of costly and deadly disasters in recent US history, and to translate these estimates into an analysis of the likely impact of COVID-19. A costly disaster series is...

In this paper we present and describe a large quarterly frequency, macroeconomic database. The data provided are closely modeled to that used in Stock and Watson (2012a). As in our previous work on FRED-MD, our goal is simply to provide a publicly available source of macroeconomic "big data"...

Skewness is a prevalent feature of macroeconomic time series and may arise exogenously because shocks are asymmetrically distributed, or endogenously, as shocks propagate through production networks. Previous theoretical work often studies these two possibilities in isolation. We nest all...

This paper seeks to better understand what makes big data analysis different, what we can and cannot do with existing econometric tools, and what issues need to be dealt with in order to work with the data efficiently. As a case study, I set out to extract any business cycle information that...

A key criticism of the existing empirical literature on the risk-return relation relates to the relatively small amount of conditioning information used to model the conditional mean and conditional volatility of excess stock market returns. To the extent that financial market participants have...

Forecasting using `diffusion indices' has received a good deal of attention in recent years. The idea is to use the common factors estimated from a large panel of data to help forecast the series of interest. This paper assesses the extent to which the forecasts are influenced by (i) how the...

Factors estimated from large macroeconomic panels are being used in an increasing number of applications. However, little is known about how the size and the composition of the data affect the factor estimates. In this paper, we question whether it is possible to use more series to extract the...