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Tests of Predictive Ability for Vector Autoregressions Used for Conditional F...
Many forecasts are conditional in nature. For example, a number of central banks routinely report forecasts conditional on particular paths of policy instruments. Even though... -
Anticipating Long-Term Stock Market Volatility (replication data)
We investigate the relationship between long-term US stock market risks and the macroeconomic environment using a two-component GARCH-MIDAS model. Our results show that... -
Bayesian VARs: Specification Choices and Forecast Accuracy (replication data)
In this paper we discuss how the point and density forecasting performance of Bayesian vector autoregressions (BVARs) is affected by a number of specification choices. We adopt... -
VAR FORECASTING USING BAYESIAN VARIABLE SELECTION (replication data)
This paper develops methods for automatic selection of variables in Bayesian vector autoregressions (VARs) using the Gibbs sampler. In particular, I provide computationally... -
Does the option market produce superior forecasts of noise-corrected volatili...
This paper assesses the robustness of the relative performance of spot? and options-based volatility forecasts to the treatment of microstructure noise. Robustness of the... -
Can inflation data improve the real-time reliability of output gap estimates?...
Potential output plays a central role in monetary policy and short-term macroeconomic policy making. Yet, characterizing the output gap involves a trend-cycle decomposition, and... -
This is what the leading indicators lead (replication data)
We propose an optimal filter to transform the Conference Board Composite Leading Index (CLI) into recession probabilities in the US economy. We also analyse the CLI's accuracy...
