How the iPhone Widens the U.S. Trade Deficit with China: The Case of the iPhone X

Through an examination of the case of the iPhone X, this paper demonstrates that Chinese companies involved in the production of the iPhone X have moved up along the global value chain. According to the bill of materials, those companies contributed 25.4% of the value added of the iPhone X. About 45% of the value added of the iPhone X originated in Japan, South Korea, and other economies. The iPhone trade remains a significant element of the statistical distortion of the China–US bilateral trade imbalance. In terms of gross value, the import of one iPhone X results in a USD332.75 trade deficit for the US; measured in terms of the value added, however the deficit is a mere USD104. The depreciation of the Chinese yuan (CNY) has very limited power to counterbalance the tariffs imposed by the Trump administration because the foreign value added embedded in Chinese exports is 33.9% on average. Simulation results show that to counterbalance a 25% tariff, the CNY would have to depreciate by 43.3% against the US dollar on average, and to fully compensate for a 25% tariff burden on the iPhone X, a 400% depreciation of the CNY would be necessary. Hedging the risk of the punitive U.S. tariffs by depreciating the CNY is impossible.     

Assessing strategic flight schedules at an airport using machine learning-based flight delay and cancellation predictions

To mitigate air traffic demand-capacity imbalances, large European airports implement strategic flight schedules, where flights are assigned arrival/departure slots several months prior to execution. We propose a generic assessment of such strategic schedules using predictions about arrival/departure flight delays and cancellations. We demonstrate our approach for strategic flight schedules in the period 2013–2018 at London Heathrow Airport. Together with the development of dedicated strategic flight schedule optimization models, our proposed approach supports an integrated strategic flight schedule assessment, where schedules are evaluated with respect to flight delays and cancellations.     

Learning about the real-world in MICE education: The case of exhibition learning from Thailand

Abstract

The use of experiential learning in tourism and hospitality education is well-documented in literature. Experiential learning studies in this field may include, for example, internship experiences, field trip perceptions, conferences, and social events. However, there is still insufficient literature to understand students’ learning and their real-world experience in MICE education, especially in the exhibition sector. This study, therefore, addresses this gap by reporting the experiential learning of graduate students in an event course with the objectives to investigate student perceptions on academic learning experiences and the development of work-related skills by carrying out the exhibition project. Students are challenged to perform a complicated task as a real exhibition organizer, and to deal with other stakeholders of the exhibition industry (e.g., exhibition venue, exhibitors, contractors, and visitors). The experiential learning method is discussed through the Plan-Do-Check-Act (PDCA) process. The results indicate that students not only gained in-depth learning about the exhibition industry, but also developed important work skills (e.g., teamwork, planning, and coordinating skills). Moreover, classroom learning, industry visits, and real-world experience are found to be the important factors contributing to exhibition learning. The current study contributes to the limited exhibition learning literature and provides event educators new insights into the teaching and learning of exhibition-based projects in regard to how students plan, learn and carry out the exhibition event through the case of Thailand. Other similar courses may apply the learning processes and results of this study to develop effective experiential learning in MICE education.     

Correction to: Optimal and robust combination of forecasts via constrained optimization and shrinkage

We provide a correction to Proposition 1 in Optimal and robust combination of forecasts via constrained optimization and shrinkage, published in the International Journal of Forecasting 38(1):97-116 (2021). This correction has no impact on any other result (neither theoretical nor empirical) provided in the above paper.     

Nowcasting South African gross domestic product using a suite of statistical models

Given lags in the release of data, a central bank must ‘nowcast’ current gross domestic product (GDP) using available quarterly or higher frequency data to understand the current state of economic activity. This paper uses various statistical modelling techniques to draw on a large number of series to nowcast South African GDP. We also show that GDP volatility has increased markedly over the last 5 years, making GDP forecasting more difficult. We show that all the models developed, as well as the Reserve Bank's official forecasts, have tended to overestimate GDP growth over this period. However, several of the statistical nowcasting models we present in this paper provide competitive nowcasts relative to the official Reserve Bank and market analysts' nowcasts.     

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo,Egypt

Abstract

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.     

基于大规模街景图片人工智能分析的精细化城市修补方法研究

在中国快速城镇化阶段“重量”而“轻质”的建设过程中，城市建成区尤其是老城区的景观环境产生了诸如景观视廊受阻、风貌破败等大量问题。对此，自2015年中央城市工作会议以来，各地相继开展了城市修补专项规划，以修复及更新城市建成环境，促进城市空间品质提升。然而，由于缺乏对城市环境整体效应的统筹考虑，故在城市实际修补过程中，城市局部地块的品质提升反而对城市整体品质构成负面影响。因此，在人工智能及大数据技术深度介入城市规划和设计实践探索的基础上，研究基于全卷积神经网络模型(FCN)和城市场景要素深度学习数据集，对城市景观环境中的各要素进行了大规模且高颗粒度的精确识别，同时与空间数据叠加，对复杂建成环境中的景观问题进行精确分析，并基于分析成果辅助后续城市规划设计实践，逐层递进地对城市复杂建成环境进行精细化修补。选择位于嵩山脚下的登封市作为案例，探索人工智能技术在辅助城市修补等规划领域的前瞻性应用。     

The effect of environmental regulations,top management commitment,and organizational learning on green product innovation: Evidence from automobile industry

This paper proposes and empirically examines a model to investigate the effect of environmental regulations, top management commitment (TMCO) and organizational learning toward green product innovation (GPI). The proposed theoretical model, grounded in dynamic capabilities view (DCV) and upper echelons theory, is analyzed by Partial least squares (PLS) method using the data from Indian automotive manufacturing firms. The findings indicate the importance of TMCO and organizational learning for implementing GPI (in response to regulations), and achieve desired performance. Further, organizational learning fully mediates between commitment of top management and GPI. The findings can be useful for managers in automotive manufacturing firms who are interested toward implementing GPI. The paper contributes to green innovation literature by empirically examining the role of TMCO and organizational learning for GPI.     

深度学习技术在海关风险甄别中的应用研究

随着互联网的高速发展和人工智能时代的到来,越来越多从前必须由人脑完成的工作能够利用计算机技术来完成,而深度学习的出现更解决了传统机器学习算法在计算机视觉领域、自然语言处理领域表现不佳的问题,使机器也能够拥有准确感知图像和语音的能力。人脸识别是深度学习网络最常见的应用场景之一,具有自然、直接、方便的特点,且不需要检测对象配合,因此非常适合用于公共安全领域的风险检测。研究充分结合海关实际需求,搭建基于深度学习技术的人脸识别模型,提供对通关旅客进行实时风险甄别的解决方案,以及海关通关风险防控场景的理论参考,为后续深度学习技术在海关业务的研究提供支撑。     

Structural breaks in the mean of dividend-price ratios: Implications of learning on stock return predictability

In their out-of-sample predictions of stock returns in the presence of structural breaks, Lettau and Van Nieuwerburgh (2008) implicitly assume that economic agents’ perception of the regime-specific mean for the dividend-price ratio is time-invariant within a regime. In this paper, we challenge this assumption and employ least squares learning with constant gain (or constant-gain learning) in estimating economic agents’ time-varying perception for the mean of dividend-price ratio. We obtain better out-of-sample predictions of stock returns than in Lettau and Van Nieuwerburgh (2008) for both the U.S. and Japanese stock markets. Our empirical results suggest that economic agents’ learning plays an important role in the dynamics of stock returns.