Using forecasts of the inflation rate in South Africa, we study the rationality of forecasts and the shape of forecasters’ loss function. When we study micro-level data of individual forecasts, we find mixed evidence of an asymmetric loss function, suggesting that inflation forecasters are...

We use South African survey data to study whether short-term inflation forecasts are unbiased. Depending on how we model a forecaster’s information set, we find that forecasts are biased due to forecaster herding. Evidence of forecaster herding is strong when we assume that the information set...

We study the directional accuracy of South African survey data of short-term and longer-term inflation forecasts. Upon applying techniques developed for the study of relative operating characteristic (ROC) curves, we find evidence that forecasts contain information with respect to the subsequent...

Using forecasts of the inflation rate in South Africa, we study the rationality of forecasts and the shape of forecasters’ loss function. When we study micro-level data of individual forecasts, we find mixed evidence of an asymmetric loss function, suggesting that inflation forecasters are...

We use South African survey data to study whether short-term inflation forecasts are unbiased. Depending on how we model a forecaster’s information set, we find that forecasts are biased due to forecaster herding. Evidence of forecaster herding is strong when we assume that the information set...

We study the directional accuracy of South African survey data of short-term and longer-term inflation forecasts. Upon applying techniques developed for the study of relative operating characteristic (ROC) curves, we find evidence that forecasts contain information with respect to the subsequent...

We use a machine-learning approach known as Boosted Regression Trees (BRT) to reexamine the usefulness of selected leading indicators for predicting recessions. We estimate the BRT approach on German data and study the relative importance of the indicators and their marginal effects on the...

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...

We use a machine-learning approach known as Boosted Regression Trees (BRT) to reexamine the usefulness of selected leading indicators for predicting recessions. We estimate the BRT approach on German data and study the relative importance of the indicators and their marginal effects on the...