Forskning på Riksbanken

Målet med den ekonomiska forskning som bedrivs inom Riksbanken är att tillhandahålla en solid begreppsmässig och empirisk grund för politiska beslut. Högkvalitativ forskning är avgörande för att garantera att Riksbanken är väl förberedd att möta de utmaningar som hänger samman med att styra penningpolitiken och främja ett säkert och effektivt betalningssystem. En viktig uppgift för den ekonomiska forskningen är att tillhandahålla modeller, verktyg och analyser för att Riksbanken ska kunna fullgöra sina åligganden.

RAMSES – a new DSGE model for monetary policy analysis

The Riksbank has recently developed a dynamic stochastic general equilibrium (DSGE) model which is used for quantitative monetary policy analysis. The model has been conformed to capture 15 Swedish macroeconomic variables using the latest progress in economic research, and is being estimated with state-of-the-art Bayesian techniques. The model takes an active part in the policy process at the Riksbank, and is used for making forecasts, interpreting the recent past, and for computing the effects of alternative policy scenarios.

The model is well-documented in the academic literature and part of the research program has been devoted to empirically validate the model and the features in it. For example the forecasting accuracy of the model has been contrasted with other, more empirically oriented, forecasting tools in the paper "Evaluating an Estimated New Keynesian Small Open Economy Model", forthcoming in the Journal of Economic Dynamics and Control.

Workshop on Understanding and Predicting Productivity Growth

On November 30 - December 1, 2007, the Riksbank will host a research workshop on “Understanding and Predicting Productivity Growth”. The aim of the meeting is to address the following questions: (i) What are the key factors for understanding total factor productivity (TFP) growth at the disaggregate level. Especially, for sectors outside of the production of information and communication technology (ICT). (ii) What are the important determinants for understanding the recent development of aggregate TFP outcomes?  (iii) Can we improve forecasts of aggregate TFP growth by using disaggregate data? (iv) What data do we need to deepen our understanding of TFP growth?  To address the last question we plan for a panel session on data issues with representatives from statistical agencies as well as academia.