As it is now well known, in the framework of DSGE models taking into accounts agents expectations, the fully unconstrained optimal policy (the Ramsey policy) has the main drawback of being time inconsistent: the authority has an incentive to recompute the optimal policy in each period in order...

It has been argued that the Great Inflation of the 1970s has been caused by a Federal Reserve policy that was not aggressive enough in combatting inflation. This led to a scenario where the U.S. economy operated under an indeterminate equilibrium with sunspot shocks becoming a driving force...

It is common in DSGE models that aim to explain the impact of monetary policy on economic variables to identify prices by assuming lump-sum transfers of money. The consequence of this is that the interest rule in these models must be of the Taylor-rule type. In this paper we explore the...

Time series estimates of inflation persistence incur an upward bias if shifts in the inflation target of the central bank remain unaccounted for. Using a structural time series approach we measure different sorts of inflation persistence allowing for an unobserved time-varying inflation target....

The performance of Monte Carlo integration methods like importance-sampling or Markov-Chain Monte-Carlo procedures depends greatly on the choice of the importance- or candidate-density. Such a density must typically be "close" to the target density to yield numerically accurate results with...

This paper uses a modified version of the DSGE model estimated in Smets and Wouters (2003) to generate a prior distribution for a vector autoregression, following the approach in Del Negro and Schorfheide (2003). This DSGE-VAR is fitted to Euro area data on GDP, consumption, investment, nominal...

The shape of the likelihood of several recently developed econometric models is often non-elliptical. Learning this shape using Gibbs sampling is discussed in this paper. A systematic analysis using graphical and computational methods is presented. Examples of the models considered in this paper...

Stochastic-variance models are important in describing and forecasting time-varying volatilities of financial time series. The introduction of jump components, in both the returns and the volatility process, improves the fit to the data. The goal of this paper is to examine the effectiveness of...