The type of time series model chosen for the comparison is, despite its simplicity, difficult to surpass, according to several international studies. Moreover, they have analysed how far ahead we can make forecasts with good accuracy, that is to say what forecasting memory the different variables have.5 They have also looked at whether there are tendencies to systematically over-estimate or under-estimate the different variables, that is, whether the forecasts have any bias. What does the analysis show about our forecasting performance? Let us first look at the entire period and compare our forecasts for GDP, employment and CPIX with outcomes. CPIX is a measure of underlying inflation, which until recently was known as UND1X. Hardly surprisingly, it appears that the forecasts have varied in accuracy (Figure 1). For instance, we have under-estimated employment growth recently (Figure 2). Nor did we predict the sharp rise in inflation spring 2001 in our assessments the previous year. However, we were more successful in capturing the downswing in inflation during 2003 (Figure 3). But how do things look in more detail for the different variables? Forecasts for GDP growth With regard to the GDP forecasts, we have periodically been accurate and close to the outcome, while we have fallen short on other occasions (Figure 1). It is often particularly difficult to make forecasts in an economic turnaround. Compared with the time series model that is used to gain a perspective of the forecasting memory, we make better GDP forecasts up to a year ahead.
Last year we also had two external researchers, Francesco Giavazzi and Frederic Mishkin, who made a more in-depth evaluation of monetary policy. This was commissioned by the Committee on Finance and covered the entire ten-year period of inflation targeting. New study of the Riksbank’s forecasting performance As a step in the evaluation work, three economists at the Monetary Policy Department of the Riksbank recently made a comprehensive analysis of our forecasting performance during the  1  “Modern forecasting models in action: Improving analyses at central banks” Adolfson, M., M. K. Andersson, J. Lindé, M. Villani & A. Vredin, (2007), International Journal of Central Banking, in the process of being published. 2  ”The Riksbank’s new indicator procedures” Andersson, M. and M. Löf, Sveriges Riksbank Economic Review 2007:1, Sveriges Riksbank. 2  BIS Review 138/2007  period 2000-2006.3 They have analysed the forecasts of the most important variables for monetary policy – GDP, employment, productivity and inflation.4 In the study they have tried to estimate how accurate the forecasts have been on average by comparing them with outcomes. To gain a perspective of the accuracy, our forecasts have also been compared with forecasts from a simple time series model. The so-called AR models, which are often used as a standard comparison in evaluations of this type, quite simply function so that one adjusts the variable in question with the aid of its own historical data.