Data sets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data, and diminishing returns suggest that we may not need terabytes of data to estimate a parameter or test a hypothesis. But which rows of data should we analyze,...

In this paper, we estimate the time-varying COVID-19 contact rate of a Susceptible-Infected-Recovered (SIR) model. Our measurement of the contact rate is constructed using data on actively infected, recovered and deceased cases. We propose a new trend filtering method that is a variant of the...

This paper examines a commonly used measure of persuasion whose precise interpretation has been obscure in the literature. By using the potential outcome framework, we define the causal persuasion rate by a proper conditional probability of taking the action of interest with a persuasive message...

We analyze cross-country trends in several aspects of technological progress over the period of 1980-2011 by examining citations data from almost 4 million utility patents granted by the US Patent and Trademark Office (USPTO). Our estimation results on patent quality and citation lags relative...

There is considerable interest in understanding how important market frictions are in stifling the transmission of ideas from one firm to another. Although the theoretical literature emphasizes the importance of these frictions, direct empirical evidence on them is limited. We use comprehensive...

This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are not point-identified and the identified set is characterized by a class of conditional moment inequalities. Exploring the semiparametric modeling restrictions, we show that...