We reformulate the Nordhaus test as a friction model where the large number of zero revisions are treated as censored, i.e., unknown values inside a small region of “imperceptibility.” Using Blue Chip individual forecasts of U.S. real GDP growth, inflation, and unemployment over 1985-2020,...

I use textual data to model German professional macroeconomic forecasters' information sets and use machine-learning techniques to analyze the efficiency of forecasts. To this end, I extract information from forecast reports using a combination of topic models and word embeddings. I then use...

We use quantile random forests (QRF) to study the efficiency of the growth forecasts published by three leading German economic research institutes for the sample period from 1970 to 2017. To this end, we use a large array of predictors, including topics extracted by means of...

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...

I evaluate German export growth and import growth forecasts published by eight professional forecasters for the years 1971 to 2019. The focus of the evaluation is on the weak and strong efficiency as well as the unbiasedness of the forecasts. To this end, I use a novel panel-data set and...