New-generation DSGE models are sometimes misspecified in dimensions that matter for their forecasting performance. The paper suggests one way to improve the forecasts of a DSGE model using a conditioning information that need not be accurate. The technique presented allows for agents to...

It is often argued that Norway’s sizeable net foreign assets based on its petroleum wealth imply an appreciation of its real exchange rate to a permanently strong level. We investigate this issue within the framework of the fundamental equilibrium real exchange rate (FEER) approach. It is...

Denne artikkelen undersøker hvor mye vi bør bruke av petroleumsinntektene hvis vi samtidig skal minimere kostnadene ved å bruke dem. Slike kostnader forbindes med sektoromstillinger som ikke kan opprettholdes og må reverseres for unngå intern og ekstern ubalanse i økonomien....

This paper addresses the so-called natural resource curse by devising a rule that can reduce macroeconomic costs associated with the consumption of revenues from natural resources. It assumes that such macroeconomic costs are mainly brought about by changes in the real exchange rate, which...

This paper examines the predictive power of weather for electricity prices in day-ahead markets in real time. We find that next-day weather forecasts improve the forecast accuracy of day-ahead electricity prices substantially, suggesting that weather forecasts can price the weather premium....

We argue that the next generation of macro modellers at Inflation Targeting central banks should adapt a methodology from the weather forecasting literature known as `ensemble modelling'. In this approach, uncertainty about model specifications (e.g., initial conditions, parameters, and boundary...

We introduce a Combined Density Nowcasting (CDN) approach to Dynamic Factor Models (DFM) that in a coherent way accounts for time-varying uncertainty of several model and data features in order to provide more accurate and complete density nowcasts. The combination weights are latent random...

A long strand of literature has shown that the world has become more global. Yet, the recent Great Global Recession turned out to be hard to predict, with forecasters across the world committing large forecast errors. We examine whether knowledge of in-sample co-movement across countries could...

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and...

This paper proposes a Bayesian estimation framework for a typical multi-factor model with time-varying risk exposures to macroeconomic risk factors and corresponding premia to price U.S. stocks and bonds. The model assumes that risk exposures and idiosynchratic volatility follow a break-point...