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 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...

We integrate a high-frequency monetary event study into a mixed-frequency macro-finance model and structural estimation. The model and estimation allow for jumps at Fed announcements in investor beliefs, providing granular detail on why markets react to central bank communications. We find that...

We provide novel evidence on the driving forces behind the sharp increase in equity values over the post-war era. From the beginning of 1989 to the end of 2017, 23 trillion dollars of real equity wealth was created by the nonfinancial corporate sector. We estimate that 54% of this increase was...

Two potential driving forces of house price fluctuations are commonly cited: credit conditions and beliefs. We posit some simple empirical calculations using direct measures of credit conditions and beliefs to consider their potentially distinct roles in house price fluctuations at the aggregate...

This paper provides a comprehensive analysis of portfolios of active mutual funds, ETFs and hedge funds through the lens of risk (anomaly) factors. We show that that these funds do not systematically tilt their portfolios towards profitable factors, such as high book-to-market (BM) ratios, high...

What explains stock market behavior in the early weeks of the coronavirus pandemic? Estimates from a dynamic asset pricing model point to wild fluctuations in the pricing of stock market risk, driven by shifts in risk aversion or sentiment. We find further evidence that the Federal Reserve...

This paper combines a data rich environment with a machine learning algorithm to provide new estimates of time-varying systematic expectational errors ("belief distortions") embedded in survey responses. We find that distortions are large on average even for professional forecasters, with all...

This paper studies a quantitative general equilibriummodel of the housing market where a large number of overlapping generations of homeowners face both idiosyncratic and aggregate risks but have limited opportunities to insure against these risks due to incomplete financial markets and...