This paper explores the hypothesis that the sources of economic and financial crises differ from non-crisis business cycle fluctuations. We employ Markov-switching Bayesian vector autoregressions (MS-BVARs) to gather evidence about the hypothesis on a long annual U.S. sample running from 1890 to...

This paper explores the hypothesis that the sources of economic and financial crises differ from non-crisis business cycle fluctuations. We employ Markov-switching Bayesian vector autoregressions (MS-BVARs) to gather evidence about the hypothesis on a long annual U.S. sample running from 1890 to...

We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output...

We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call dynamic pools, and use it to investigate the relative forecasting performance of dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, for output...

We provide a novel methodology for estimating time-varying weights in linear prediction pools, which we call Dynamic Pools, and use it to investigate the relative forecasting performance of DSGE models with and without financial frictions for output growth and inflation from 1992 to 2011. We...

We propose a method to incorporate information from Dynamic Stochastic General Equilibrium (DSGE) models into Dynamic Factor Analysis. The method combines a procedure previously applied for Bayesian Vector Autoregressions and a Gibbs Sampling approach for Dynamic Factor Models. The factors in...

While the usefulness of factor models has been acknowledged over recent years, little attention has been devoted to the forecasting power of these models for the Japanese economy. In this paper, we aim at assessing the relative performance of factor models over different samples, including the...

This collection of papers analyzes the versatility and predictive power of survey expectations data in asset pricing and macroeconomic forecasting. The first paper, Using Sentiment Surveys to Predict GDP Growth and Stock Returns sheds new light on the question of whether or not sentiment...

Inflation rates are highly persistent and extremely difficult to predict. Most statistical predictions based on predictive regressions fail to outperform the simple assumption of random walk in out-of-sample testing. The poor out-of-sample performance is a common feature of predictive...

Interest rate data are an important element of macroeconomic forecasting. Projections of future interest rates are not only an important product themselves, but also typically matter for forecasting other macroeconomic and financial variables. A popular class of forecasting models is linear...