In this note, we use multivariate models estimated with Bayesian techniques and an out-ofsample approach to investigate whether money growth Granger-causes output growth in the United States. We find surprisingly strong evidence for a money-output link over the 1960-2005 period. However, further...

This paper shows how to estimate forecast uncertainty about future short-term interest rates by combining a time-varying Taylor rule with an unobserved components model of economic fundamentals. Using this model I separate interest rate uncertainty into economically meaningful components that...

Using real-time data I estimate out-of-sample forecast uncertainty about the Federal Funds Rate. Combining a Taylor rule with a model of economic fundamentals I disentangle economically interpretable components of forecast uncertainty: uncertainty about future economic conditions and uncertainty...

This paper provides a general strategy for analyzing monetary policy in real time which accounts for data uncertainty without explicitly modelling the revision process. The strategy makes use of all the data available from a real-time data matrix and averages model estimates across all data...

We provide a critical assessment of the method used by the Cleveland Fed to correct expected inflation derived from index-linked bonds for liquidity and inflation risk premia and show how their method can be adapted to account for time-varying inflation risk premia. Furthermore, we show how...

We present a new way to model age-specific demographic variables with the example of age-specific mortality in the U.S., building on the Lee-Carter approach and extending it in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables underlying...

We present a new way to model age-specific demographic variables, using the example of age-specific mortality in the United States, building on the LeeCarter approach and extending it in several dimensions. We incorporate covariates and model their dynamics jointly with the latent variables...

We propose a new model for volatility forecasting which combines the Generalized Dynamic Factor Model (GDFM) and the GARCH model. The GDFM, applied to a large number of series, captures the multivariate information and disentangles the common and the idiosyncratic part of each series of returns....

We propose two new procedures for comparing the mean squared prediction error (MSPE) of a benchmark model to the MSPEs of a small set of alternative models that nest the benchmark. Our procedures compare the benchmark to all the alternative models simultaneously rather than sequentially, and do...

We propose a new approach to model high and low frequency components of equity correlations. Our framework combines a factor asset pricing structure with other specifications capturing dynamic properties of volatilities and covariances between a single common factor and idiosyncratic returns....