Using unobservable conditional variance as measure, latentvariable approaches, such as GARCH and stochasticvolatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of highfrequency financial market data modeling realized...

Using unobservable conditional variance as measure, latent–variable approaches, such as GARCH and stochastic–volatility models, have traditionally been dominating the empirical finance literature. In recent years, with the availability of high–frequency financial market data modeling...

This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated U.S. recessions. We generate forecasts from six different models of the U.S. economy and compare them to professional forecasts from the Federal...

We focus on a quantitative assessment of rigid labor markets in an environment of stable monetary policy. We ask how wages and labor market shocks feed into the inflation process and derive monetary policy implications. Towards that aim, we structurally model matching frictions and rigid wages...

This paper investigates the accuracy and heterogeneity of output growth and inflation forecasts during the current and the four preceding NBER-dated U.S. recessions. We generate forecasts from six different models of the U.S. economy and compare them to professional forecasts from the Federal...

We focus on a quantitative assessment of rigid labor markets in an environment of stable monetary policy. We ask how wages and labor market shocks feed into the inflation process and derive monetary policy implications. Towards that aim, we structurally model matching frictions and rigid wages...

The predictive likelihood is of particular relevance in a Bayesian setting when the purpose is to rank models in a forecast comparison exercise. This paper discusses how the predictive likelihood can be estimated for any subset of the observable variables in linear Gaussian state-space models...

Renewed interest in fiscal policy has increased the use of quantitative models to evaluate policy. Because of modeling uncertainty, it is essential that policy evaluations be robust to alternative assumptions. We find that models currently being used in practice to evaluate fiscal policy...

We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects...

In this paper we investigate the comparative properties of empirically-estimated monetary models of the U.S. economy. We make use of a new data base of models designed for such investigations. We focus on three representative models: the Christiano, Eichenbaum, Evans (2005) model, the Smets and...