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Working Paper
Does Uncertainty Really Predict Recessions?
We evaluate the ability of economic uncertainty measures to forecast recessions in real time. We find that including uncertainty increases the predictive power of both in sample and out-of-sample forecast models relative to a baseline set of financial variables. A nonlinear maximum transformation of uncertainty, which captures whether a measure exceeds its maximum over the past year, improves forecast performance for some measures. Adding a contemporaneous indicator like GDP growth alongside uncertainty yields additional predictive gains. Lastly, ex post Bayesian model averaging outperforms ...
Working Paper
Trend-Cycle Decomposition and Forecasting Using Bayesian Multivariate Unobserved Components
We propose a generalized multivariate unobserved components model to decompose macroeconomic data into trend and cyclical components. We then forecast the series using Bayesian methods. We document that a fully Bayesian estimation, that accounts for state and parameter uncertainty, consistently dominates out-of-sample forecasts produced by alternative multivariate and univariate models. In addition, allowing for stochastic volatility components in variables improves forecasts. To address data limitations, we exploit cross-sectional information, use the commonalities across variables, and ...
Working Paper
Forecasts of inflation and interest rates in no-arbitrage affine models
In this paper, we examine the forecasting ability of an affine term structure framework that jointly models the markets for Treasuries, inflation-protected securities, inflation derivatives, and oil future prices based on no-arbitrage restrictions across these markets. On the methodological side, we propose a novel way of incorporating information from these markets into an affine model. On the empirical side, two main findings emerge from our analysis. First, incorporating information from inflation options can often produce more accurate inflation forecasts than those based on the Survey of ...