Forecasting macroeconomic variables using disaggregate survey data

We assess the forecast ability of Norges Bank’s regional survey for inflation, GDP growth and the unemployment rate in Norway. We propose several factor models based on regional and sectoral information given by the survey. The analysis identifies which information extracted from the ten sectors and the seven regions performs particularly well at forecasting different variables and horizons. Results show that several factor models beat an autoregressive benchmark in forecasting inflation and unemployment rate. However, the factor models are most successful in forecasting GDP growth. Forecast combinations based on past performance give in most cases more accurate forecasts than the benchmark, but they never give the most accurate forecasts.