This paper uses machine learning (ML) to estimate hedonic price indices at scale from item-level transaction and product characteristics. The procedure uses state-of-the-art approaches from hedonic econometrics and implements them with a neural network ML approach. Applying the methodology to...

This paper uses machine learning (ML) to estimate hedonic price indices at scale from item-level transaction and product characteristics. The procedure uses state-of-the-art approaches from hedonic econometrics and implements them with a neural network ML approach. Applying the methodology to...

Private equity critics claim that leveraged buyouts bring huge job losses and few gains in operating performance. To evaluate these claims, we construct and analyze a new dataset that covers U.S. buyouts from 1980 to 2005. We track 3,200 target firms and their 150,000 establishments before and...

Private equity critics claim that leveraged buyouts bring huge job losses and few gains in operating performance. To evaluate these claims, we construct and analyze a new dataset that covers U.S. buyouts from 1980 to 2005. We track 3,200 target firms and their 150,000 establishments before and...

Ayash and Rastad (2017) express several concerns about our 2014 analysis of private equity buyouts. We welcome their interest in our work but think their criticisms are off the mark. Some of their claims reflect a misunderstanding of the Census Bureau's Longitudinal Business Database (LBD) and...

Since 1970, housing's relative price, share of expenditure, and ``unaffordability'' have all grown. We estimate housing demand using a novel compensated framework over space and an uncompensated framework over time. Our specifications pass tests imposed by rationality and household mobility....