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Over the last 15 years, studies on hierarchical forecasting have moved away from single-level approaches towards proposing linear combination approaches across multiple levels of the hierarchy. Such combinations offer coherent reconciled forecasts, improved forecasting performance and aligned decision-making. This paper proposes a novel hierarchical forecasting approach based on machine learning. The proposed method allows for non-linear combinations of the base forecasts, thus being more general than linear approaches. We structurally combine the objectives of improved post-sample empirical forecasting accuracy and coherence. Due to its non-linear nature, our approach selectively combines the base forecasts in a direct and automated way without requiring that the complete information must be used for producing reconciled forecasts for each series and level. The proposed method is evaluated both in terms of accuracy and bias using two different data sets coming from the tourism and retail industries. Our results suggest that the proposed method gives superior point forecasts than existing approaches, especially when the series comprising the hierarchy are not characterized by the same patterns. • We use machine learning approaches for hierarchical reconciliation. • We offer a non-linear approach to the problem of hierarchical coherence. • Our approach explicitly focuses on post-sample in estimating the combination weights. • Our approach offers improved forecasting performance on two real-life data sets. [ABSTRACT FROM AUTHOR]

• We examined the impact of gamification on learning about judgmental biases. • We ran a 2 (read yes | no) x 2 (gamification yes | no) between-subject experiment. • Our findings reveal positive effects of gamification on learning. • Students exhibited better learning outcomes under gamified condition compared to reading-based condition • The impact of gamification differed between engineering and business school students. In their daily lives, people are confronted with situations where they need to form a schema of possible future scenarios and the likelihood of them occurring, be it about climate change, economic up- or downturn, or even the potential success of a romantic date. Be these issues of mundane or universal importance, this judgmental forecasting poses people with a difficult pervasive cognitive challenge. Commonly, judgmental forecasting is taught in forecasting courses syllabi, and the pedagogy surrounding it is challenging. However, gamification and game-based learning have risen as promising tools to simulate different kinds of scenarios and stimulate cognitive problem solving. This study investigates the effects of a gamified application with points, levels, challenges, storytelling and leaderboard for teaching judgmental forecasting by conducting a 2 × 2 between-subjects experiment (treatments: i) read: yes vs no, and ii) gamification: yes vs no), with a sample of 285 students of a School of Electrical and Computer Engineering and a Business Administration Department. The findings indicate that the gamified application improved learning outcomes regarding the heuristics and biases that affect judgmental forecasting by almost 15%, supporting the use of gamification in forecasting education. [ABSTRACT FROM AUTHOR]