Fly with the Eagles or Scratch with the Chickens? – Herd Behavior of Exchange Rate Forecasters We analyze whether exchange-rate forecasters herd. To this end, we lay out two widely studied theoretical models of forecaster herding. The models illustrate why forecasters may herd. We then...

We use a Panel Smooth Transition Regression (STR) model to study nonlinearities in the expectation-formation process in the U.S. stock market. To this end, we use data from the Livingston survey to investigate how the importance of regressive and extrapolative expectations fluctuates over time...

Much significant research has been done to shed light on discrimination of females in, for example, labor markets. Less is known, in contrast, about the amount of discrimination in the virtual world of online gaming. In an early study, Castronova (2004) finds that female avatars receive about...

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 analyze the law of one price (LoP) based on BigMac and Fortnite prices. We find a positive but less than perfect correlation between the over-/undervaluations of the two indices. While we cannot reject the LoP for the Fortnite data, we find that it does not hold for the BigMac data.

We examine the forecasting power of a daily newspaper‐based index of uncertainty associated with infectious diseases (EMVID) for real estate investment trusts (REITs) realized market variance of the United States (US) via the heterogeneous autoregressive realized volatility (HAR‐RV) model....

Utilizing a machine learning technique known as random forests, we study whether regional output growth uncertainty helps to improve the accuracy of forecasts of regional output growth for 12 regions of the UK using monthly data for the period from 1970 to 2020. We use a stochastic volatility...