We develop a Markov Switching model for inflation with time-varying transition probabilities. Inflation is characterized by two regimes (high and low inflation) and the probability of regime changes depends on money growth. Using Bayesian techniques, we apply the model to the euro area, Germany,...

We study the relationship between monetary policy and long‐term rates in a structural, general equilibrium model estimated on both macro‐ and yield‐data from the United States. Regime shifts in the conditional variance of productivity shocks, or "uncertainty shocks," are a crucial driver...

Macroeconomic practitioners frequently work with multivariate time series models such as VARs, factor augmented VARs as well as time-varying parameter versions of these models (including variants with multivariate stochastic volatility). These models have a large number of parameters and, thus,...

We use factor augmented vector autoregressive models with time-varying coefficients and stochastic volatility to construct a financial conditions index that can accurately track expectations about growth in key US macroeconomic variables. Time-variation in the models׳ parameters allows for the...

In this paper, we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints, we draw on ideas from the dynamic model averaging literature which achieve reductions in the computational burden...

This paper considers Bayesian variable selection in regressions with a large number of possibly highly correlated macroeconomic predictors. I show that acknowledging the correlation structure in the predictors can improve forecasts over existing popular Bayesian variable selection algorithms.