We study the benefits of forecast combinations based on forecast-encompassing tests relative to uniformly weighted forecast averages across rival models. For a realistic simulation design, we generate multivariate time-series samples of size 40 to 200 from a macroeconomic DSGE-VAR model....

This paper identiftes U.S. monetary and ftscal dominance regimes using machine learning techniques. The algorithms are trained and verifted by employing simulated data from Markov-switching DSGE models, before they classify regimes from 1968-2017 using actual U.S. data. All machine learning...

The post-crisis environment has posed important challenges to standard forecasting models. In this paper, we exploit several combinations of a large-scale DSGE structural model with standard reduced-form methods such as (B)VAR (i.e. DSGE-VAR and Augmented-(B)VARDSGE methods) and assess their use...

This paper describes NEMO, the main dynamic stochastic general equilibrium model used at Norges Bank for monetary policy analysis and forecasting. NEMO has been used to identify the sources of business cycle fluctuations in Norway, to conduct scenario analysis, to produce macroeconomic...

Recent work has analyzed the forecasting performance of standard dynamic stochastic general equilibrium (DSGE) models, but little attention has been given to DSGE models that incorporate nonlinearities in exogenous driving processes. Against that background, we explore whether incorporating...

This paper outlines an approach to assess uncertainty around a forecast baseline as well as the impact of alternative policy rules on macro variability. The approach allows for non-Gaussian shock distributions and non-linear underlying macroeconomic models. Consequently, the resulting...

This paper demonstrates a failure of standard, generalized Schur (orQZ) decomposition based solutions methods for linear dynamic stochastic general equilibrium (DSGE) models when there is insufficient eigenvalue separation about the unit circle. The significance of this is demonstrated in a...

This paper identifies U.S. monetary and fiscal dominance regimes using machine learning techniques. The algorithms are trained and verified by employing simulated data from Markov-switching DSGE models, before they classify regimes from 1968-2017 using actual U.S. data. All machine learning...