(with Anna Cieslak, Michael McMahon, and Song Xiao)
We use the structure of FOMC meetings to measure the uncertainty policymakers face about different macroeconomic conditions, and the associated impact on policy. Uncertainty amplifies the reaction function coefficients, especially for inflation.
(with Michael McMahon and Matthew Tong)
Journal of Monetary Economics (2019) 108 (December): 185-202.
When central banks communicate signals on uncertainty in economic conditions, they can have their largest impact on long-run yields. This novel form of information effect explains the market reaction to the Bank of England's Inflation Report very well.
(with Michael McMahon and Andrea Prat)
Quarterly Journal of Economics (2018) 133 (2): 801-870
Using communication measures from machine learning, we find evidence for both the conformity and discipline effects predicted by the career concerns literature following an increase in transparency on the Federal Open Market Committee. On balance, the discipline effect appears stronger, as rookie members become more influential.
(with Michael McMahon)
The Review of Economic Studies (2016) 83 (4): 1645-1672
A new model of reputation for monetary policy makers predicts that all preference types become softer on inflation over time and that this evolution is more pronounced for types that put more weight on output, predictions we confirm using voting data from the Bank of England.
(with Michael McMahon)
Journal of International Economics (2016), 38th NBER International Seminar on Macroeconomics: S114-S133.
We adopt an automated approach to measuring the extent to which Fed statements provide forward guidance versus information on economic conditions. Overall, the former has larger macroeconomic effects, particularly on market variables.
(with Michael McMahon and Carlos Velasco)
Journal of Monetary Economics (2014) 67 (October): 16-32.
Experts on the Bank of England's Monetary Policy Committee differ both in terms of preferences and private forecasts. A committee of internal Bank members significantly outperforms an individual because of information pooling, but both large committees and those that add externals appear to add little value.
(with Tejas Ramdas, Raffaella Sadun, and Joseph Fuller)
We measure the demand for executive skills aross firms using a unique, large-scale corpus of job specifications. The importance of social skills is growing over time and is related to firm size, firm scope, and the information intensity of worker skills.
(with Steven Davis and Cristhian Seminario-Amez)
We combine elements of supervised machine learning and dictionary methods to uncover narrow, interpretable risk exposures from 10-K filings that account for firm-level reactions to aggregate shocks. We apply this idea to study equity returns during the COVID-19 pandemic.
(with Oriana Bandiera, Andrea Prat, and Raffaella Sadun)
Journal of Political Economy (2020) 128 (4): 1325-1369.
We use a machine learning algorithm applied to granular CEO survey data to construct a scalar behavioral index. The index is strongly correlated with firm performance, and this relationship appears only several years after CEO appointment. Evidence suggests the correlation is due to assignment frictions, which are substantially worse in low-income countries.
(with Massimo Motta)
Journal of Industrial Economics (2019) 67 (3-4): 409-447.
When downstream firms are sufficiently risk averse, or subject to limited liability, and cannot observe their competitors' shocks, an upstream firm offers contracts that offer all input to one downstream firm.
(with Benito Arruñada)
International Review of Law and Economics, (2015) 42 (June): 185-191
We describe how one can interpret public sector organizations through the lens of managerial accounting, and provide anecdotal evidence that mixing strong incentives with bureaucratic administration works well in various settings.
Journal of Law, Economics, and Organization (2013) 29 (6): 1279-1316
When workers have career concerns, performance feedback increases the uncertainty of future effort but allows them to manipulate their employers' beliefs on future effort; the optimal policy only reveals intermediate performance levels.
(with Gergely Buda, Vasco Carvalho, Alvaro Ortiz, Tomasa Rodrigo, and Sevi Rodriguez Mora)
We use comprehensive financial transactions from one of Europe's largest banks to build a large-scale consumption survey using national accounting principles. We use this to build aggregate and distributional accounts for consumption, as well as a detailed individual consumption panel.
(with Vasco Carvalho, Juan Garcia, Alvaro Ortiz, Tomasa Rodrigo, Jose V Rodriguez Mora, and Jose Ruiz)
Royal Society Open Science (2021) 8: 210218
We use a database of 1.4 billion payments transactions to track the impact of the COVID-19 pandemic and policy response across time, space, and sectors in Spain.
(with Jack Jewson, Li Li, Laura Battaglia, David Rossell, and Piotr Zweirnik)
We show how observed connections among variables in multiple networks can improve estimation of the precision matrix by targeting regularization and develop a Bayesian model for inferring pairwise correlations.
(with Szymon Sacher and Laura Battaglia)
We show how GPU-accelerated automatic differentation can be used to conduct inference for Bayesian models of text and other discrete data.
(with Michael McMahon and Tang Srisuma)
Journal of Applied Econometrics (2016) 31 (4): 762-771
Econometricians can use variation in the prior distribution to substantially improve the accuracy of existing techniques for estimating decision-makers' preferences and private signal distributions.
(with David Bholat, Pedro Santos, and Cheryl Schonhardt-Bailey)
Center for Central Banking Studies Handbook No. 33, Bank of England