Forecasting correlations between stocks and commodities is important for diversification across asset classes and other risk management decisions. Correlation forecasts are affected by model uncertainty, the sources of which can include uncertainty about changing fundamentals and associated...

Baseline assumptions play a crucial role in conducting consistent quantitative policy assessments for dynamic Computable General Equilibrium (CGE) models. Two essential factors that influence the determination of the baselines are the data sources of projections and the applied calibration...

The pricing kernel is an important tool for understanding asset prices, expected returns, and investor preferences. However, empirical findings often reveal deviations from theoretical predictions, leading to the so-called "pricing kernel puzzle". This article explores the pricing kernel under...

In this paper we put forward a Bayesian Model Averaging method dealing with model uncertainty in the presence of potential spatial autocorrelation. The method uses spatial filtering in order to account for different types of spatial links. We contribute to existing methods that handle spatial...

In this paper we analyze the impact of three U.S. structural shocks on, and its transmission 0to, the world economy. For that purpose we use a Bayesian version of the global vector autoregressive (GVAR) model coupled with a prior specification that explicitly treats uncertainty regarding...

Standard economics models require that financial incentives improve performance, while leading theories in psychology allow for the opposite. Experimental results are mixed, and so far have not been corrected for publication bias and model uncertainty. We collect 1,568 economics estimates...

Optimal monetary policy studies typically rely on a single structural model and identification of model-specific rules that minimize the unconditional volatilities of inflation and real activity. In our proposed approach, we take a large set of structural models and look for the model-robust...

Climate change has become one of the most prominent concerns globally. In this paper, we study the transition risk of greenhouse gas emission reduction in structural environmental-macroeconomic DSGE models. First, we analyze the uncertainty in model prediction on the effect of unanticipated and...

We study an optimal robust monetary policy for a small open economy. The robust control approach assumes that economic agents cannot assign probabilities to a set of plausible models and rather focuses on the worst possible misspecification from a benchmark model. Our findings suggest that,...

This paper provides concise, nontechnical, step-by-step guidelines on how to conduct a modern meta-analysis, especially in social sciences. We treat publication bias, p-hacking, and heterogeneity as phenomena meta-analysts must always confront. To this end, we provide concrete methodological...