We study the transmission of monetary policy through bank securities portfolios using granular supervisory data on U.S. bank securities, hedging positions, and corporate credit. Banks that experienced larger losses on their securities during the 2022-2023 monetary tightening cycle extended less...

From 2011 to 2014, the Brazilian government conducted a heavily advertised major credit expansion program through government banks as part of its effort to stimulate the economy. Using administrative data on individual-level borrowing and spending, we find that the program led to a substantial...

The rise of inflation in 2021 and 2022 surprised many macroeconomists who ignored the earlier surge in money growth because past instability in the demand for simple-sum monetary aggregates had made these aggregates unreliable indicators. We find that the demand for more theoretically-based...

We revisit time-variation in the Phillips curve, applying new Bayesian panel methods with breakpoints to US and European Union disaggregate data. Our approach allows us to accurately estimate both the number and timing of breaks in the Phillips curve. It further allows us to determine the...

We show that decentralized privately created money with unstable values can hinder the traded, more transaction-friction sensitive, sector of the economy. We do so in the context of the NationalBanking Act of 1864 in the United States that created a new federally-regulated, fully-backed currency...

This paper studies the transmission of monetary policy to the stock market through investors' discount factors. To isolate this channel, we investigate the effect of US monetary policy surprises on the ratio of prices of the same stock listed simultaneously in Hong Kong and Mainland China, and...

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance--in terms of SDF Sharpe ratio and test asset pricing errors--is improving in model parameterization (or "complexity"). Our empirical findings verify the...

This paper uses computational techniques to assess whether or not various propositions that have been advanced as plausible in the literature on Customs Unions (or other regional trade agreements) may actually hold. The idea is to make probabilistic statements as to whether propositions of...