We study the efficiency of growth and inflation forecasts published by three leading German economic research institutes during a period of time ranging from 1970 to 2017. To this end, we examine whether the information used by the research institutes when they formed their forecasts helps to explain the ex-post realized forecast errors. We identify the information that the research institutes used to set up their quantitative forecasts by applying computational-linguistics techniques to decompose the business-cycle reports published by the research institutes into various topics. Our results show that several topics have predictive value for the forecast errors.