Using both standard regressions and Markov-Chain Monte-Carlo estimation methods from a Bayesian perspective and a sample of 39 countries for 1981-2006, we re-examine the role of central-bank independence (CBI) in macroeconomic outcomes. Control variables commonly used in the literature are...

We estimate the approximate nonlinear solution of a small DSGE model on euro area data, using the conditional particle filter to compute the model likelihood. Our results are consistent with previous findings, based on simulated data, suggesting that this approach delivers sharper inference...

In this paper we extend the parametric, asymmetric, stochastic volatility model (ASV), where returns are correlated with volatility, by flexibly modeling the bivariate distribution of the return and volatility innovations nonparametrically. Its novelty is in modeling the joint, conditional,...

An expanding literature articulates the view that Taylor rules are helpful in predicting exchange rates. In a changing world however, Taylor rule parameters may be subject to structural instabilities, for example during the Global Financial Crisis. This paper forecasts exchange rates using such...

We study the relationship between growth and variability in a DSGE model with nominal rigidities and growth driven by learning-by-doing. We show that this relationship may be positive or negative depending on the impulse source of fluctuations A key role is also played by the Frisch elasticity...

This paper critically appraises the di erent approaches that have characterized the literature on the macroeconomic e ects of job reallocations from Lilien's seminal work to recent developments rooted in structural general equilibrium models, nonlinear econometric techniques and the concepts of...

This paper critically appraises the approaches that have characterized the literature on the macroeconomic effects of job reallocations. Since Lilien's (1982) seminal contribution there has been a flourishing of empirical analysis but no unifying theoretical framework has obtained consensus in...

In this paper we develop methods for estimation and forecasting in large time-varying parameter vector autoregressive models (TVP-VARs). To overcome computational constraints with likelihood-based estimation of large systems, we rely on Kalman filter estimation with forgetting factors. We also...

We develop methods for Bayesian inference in vector error correction models which are subject to a variety of switches in regime (e.g. Markov switches in regime or structural breaks). An important aspect of our approach is that we allow both the cointegrating vectors and the number of...

The Hodrick-Prescott (HP) method is a popular smoothing method for economic time series to get a smooth or long-term component of stationary series like growth rates. We show that the HP smoother can be viewed as a Bayesian linear model with a strong prior using differencing matrices for the...