Using only daily data on bond and stock returns, we identify and characterize flight to safety (FTS) episodes for 23 countries. On average, FTS days comprise less than 3% of the sample, and bond returns exceed equity returns by 2.5 to 4%. The majority of FTS events are country-specific not...

We use non-Gaussian features in U.S. macroeconomic data to identify aggregate supply and demand shocks while imposing minimal economic assumptions. Recessions in the 1970s and 1980s were driven primarily by supply shocks, later recessions were driven primarily by demand shocks, and the Great...

A joint model of macroeconomic and term structure dynamics is specified and estimated for the euro area. The model comprises a backward-looking Phillips curve, a dynamic IS equation, a monetary policy rule as well as a specification of the dynamics of trend growth and the natural real interest...

This paper studies a nonlinear one-factor term structure model in discrete time. The single factor is the short-term interest rate, which is modeled as a self-exciting threshold autoregressive (SETAR) process. Our specification allows for shifts in the intercept and the variance. The process is...

Using a stochastic discount factor approach, we derive the exact solution for arbitrage-free bond yields for the case that the short-term interest rate follows a threshold process with the intercept switching endogenously. The yield functions, mapping the one-month rate into n-period yields,...

We extract aggregate demand and supply shocks for the US economy from real-time survey data on inflation and real GDP growth using a novel identification scheme. Our approach exploits non-Gaussian features of macroeconomic forecast revisions and imposes minimal theoretical assumptions. After...

We propose a classical approach to estimate factor-augmented vector autoregressive (FAVAR) models with time variation in the factor loadings, in the factor dynamics, and in the variance-covariance matrix of innovations. When the time-varying FAVAR is estimated using a large quarterly dataset of...