We develop a deep learning model to detect emotions embedded in press conferences after the meetings of the Federal Open Market Committee and examine the influence of the detected emotions on financial markets. We find that, after controlling for the Fed's actions and the sentiment in policy...

We show that "preemptive" capital flow management measures (CFM) can reduce emerging markets and developing countries' (EMDE) external finance premia during risk-off shocks, especially for vulnerable countries. Using a panel dataset of 56 EMDEs during 1996-2020 at monthly frequency, we document...

We develop a structural VAR in which an occasionally-binding constraint generates censoring of one of the dependent variables. Once the censoring mechanism is triggered, we allow some of the coefficients for the remaining variables to change. We show that a necessary condition for a unique...

Using confidential regulatory firm-bank-loan level data from the U.S., we document four new facts about the credit market. First, private SMEs typically utilize all available bank credit which comprises their entire balance sheet debt, compared to large listed firms who can switch between...

Using a new survey of European households, we study how exogenous variation in the macroeconomic uncertainty perceived by households affects their spending decisions. We use randomized information treatments that provide different types of information about the first and/or second moments of...