We propose a Bayesian infinite hidden Markov model to estimate time- varying parameters in a vector autoregressive model. The Markov structure allows for heterogeneity over time while accounting for state-persistence. By modelling the transition distribution as a Dirichlet process mixture model,...

Despite years of research, there is still uncertainty around the effects of monetary policy shocks. We reassess the empirical evidence by combining a new identification that accounts for informational rigidities, with a flexible econometric method robust to misspecifications that bridges between...

The restrictions implied by the theory of time-consistent monetary policy are imposed on empirical data. Model estimation is conducted using Bayesian Markov chain Monte Carlo techniques. We are able to identify two major regimes regarding the policy of the Federal Reserve from 1970 to 2008....

The New Keynesian Phillips curve implies that the output gap, the deviation of the actual output from its natural level due to nominal rigidities, drives the dynamics of inflation relative to expected inflation and lagged inflation. This paper exploits the empirical success of the New Keynesian...

We propose a benchmark prior for the estimation of vector autoregressions: a prior about initial growth rates of the modeled series. We first show that the Bayesian vs frequentist small sample bias controversy is driven by different default initial conditions. These initial conditions are...

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 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...

This paper develops a Bayesian quantile regression model with time-varying parameters (TVPs) for forecasting inflation risks. The proposed parametric methodology bridges the empirically established benefits of TVP regressions for forecasting inflation with the ability of quantile regression to...

We propose a benchmark prior for the estimation of vector autoregressions: a prior about initial growth rates of the modeled series. We first show that the Bayesian vs frequentist small sample bias controversy is driven by different default initial conditions. These initial conditions are...

This paper identifies five factors that can capture 95% of the variance across 39 US dollar exchange rates based on the principal component method. A time-varying parameter factor-augmented vector autoregressive (TVP-FAVAR) model is used to analyze the determinants of movements in these exchange...