One-year-ahead forecasts by the OECD and by national institutes of GDP growth and inflation in 13 European countries are analysed. RMSE was large: 1.9 % for growth and 1.6 % for inflation. Six (11) OECD and 10 (7) institute growth forecast records were significantly better than an average growth forecast (the current year forecast). All full record-length inflation forecasts were significantly better than both naive alternatives. There was no significant difference in accuracy between the forecasts of the OECD and the institutes. Two forecasts were found to be biased and one had auto correlated errors. Directional forecasts were significantly better than a naive alternative in one-half of the cases. Overall, inflation forecasts were significantly more accurate than growth forecasts, and in contrast to growth forecasts, they generally improved over time. This has implications for economic policy. Positively biased revisions reveal large errors in data.

Abstract. A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points.Instead of maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies.Some theoretical arguments for this are given.

In this paper variance stabilizing …lters are discussed. A new …lter with nice properties is proposed which makes use of moving averages and moving standard deviations, the latter smoothed with the Hodrick-Prescott …lter. This …lter is compared to a GARCH-type …lter. An ARIMA model is estimated for the …ltered GDP series, and the parameter estimates are used in forecasting the un…ltered series. These forecasts compare well with those of ARIMA, ARFIMA and GARCH models based on the un…ltered data. The …lter does not colour white noise.