Recent research has found that macroeconomic survey forecasts of uncertainty exhibit several deficiencies, such as horizon-dependent biases and lower accuracy than simple unconditional uncertainty forecasts. We examine the inflation uncertainty forecasts from the Bank of England, the Banco...

The interest rate assumptions for macroeconomic forecasts differ considerably among central banks. Common approaches are given by the assumption of constant interest rates, interest rates expected by market participants, or the central bank’s own interest rate expectations. From a theoretical...

Using a survey with 57 German firms, we evaluate the level of digitalization of the human resource management (HRM) function and document perceived benefits and barriers of technology adoption from organizational and individual users’ perspectives. The results give reason for optimism. Most of...

As technology-assisted decision-making is becoming more widespread, it is important to understand how the algorithmic nature of the decisionmaker affects how decisions are perceived by the affected people. We use a laboratory experiment to study the preference for human or algorithmic decision...

Are people algorithm averse, as some previous literature indicates? If so, can the retention of human oversight increase the uptake of algorithmic recommendations, and does keeping a human in the loop improve accuracy? Answers to these questions are of utmost importance given the fast-growing...

We examine the impact of labor market power on firms' adoption of automation technologies. We develop a model that incorporates labor market power into the task-based theory of automation. We show that, due to higher marginal cost of labor, monopsonistic firms have stronger incentives to...

Recently, several institutions have increased their forecast horizons, and many institutions rely on their past forecast errors to estimate measures of forecast uncertainty. This work addresses the question how the latter estimation can be accomplished if there are only very few errors available...

Forecasts are useless whenever the forecast error variance fails to be smaller than the unconditional variance of the target variable. This paper develops tests for the null hypothesis that forecasts become uninformative beyond some limiting forecast horizon h. Following Diebold and Mariano (DM,...

The linear pool is the most popular method for combining density forecasts. We analyze the linear pool's implications concerning forecast uncertainty in a new theoretical framework that focuses on the mean and variance of each density forecast to be combined. Our results show that, if the...