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One of the most discussed problems in the financial world is stock option pricing. The Black-Scholes equation is a parabolic partial differential equation which provides an option pricing model. The present work proposes an approach based on neural networks to solve the Black-Scholes equations. Real-world data from the stock options market were used as the initial boundary to solve the Black-Scholes equation. In particular, times series of call options prices of Brazilian companies Petrobras and Vale were employed. The results indicate that the network can learn to solve the Black-Scholes equation for a specific real-world stock options time series. The experimental results showed that the neural network option pricing based on the Black-Scholes equation solution can reach an option pricing forecasting more accurate than the traditional Black-Scholes analytical solutions. The experimental results making it possible to use this methodology to make short-term call option price forecasts in options markets. [ABSTRACT FROM AUTHOR]

Establishing a special economic zone (SEZ) is the most important place-based policy for developing countries to promote regional growth since the 20th century. SEZs have played an important role in China's reform and open-up policy. In this study, we investigate the effect of China's SEZs on firms' trade regime: choosing between processing and ordinary trade- implicitly a choice of production technology and position in global supply chain. We treat the establishment of SEZs as a quasi-natural experiment and apply a Difference-in-Differences (DID) method using firm-level data sourced from Annual Survey of Industrial Firms (ASIF) and the Chinese Customs Trade Statistics (CCTS). The results suggest that firms within SEZs are associated with lower shares of ordinary trade in total trade value compared to non-SEZ firms. This indicates that while benefiting from various preferential policies, SEZ firms increase total exports but at the expense of positioning themselves lower in the global value chain. However, both industrial agglomeration and geographic clustering could mitigate this negative impact. Moreover, firms inside provincial-level SEZs are more engaged in processing trade than those in national-level SEZs. This study offers cautionary insights for analyzing exporter performance and regional development in other developing countries integrated into global value chains. [ABSTRACT FROM AUTHOR]

In the past decade, financial institutions have invested significant efforts in the development of accurate analytical credit scoring models. The evidence suggests that even small improvements in the accuracy of existing credit-scoring models may optimize profits while effectively managing risk exposure. Despite continuing efforts, the majority of existing credit scoring models still include some judgment-based assumptions that are sometimes supported by the significant findings of previous studies but are not validated using the institution's internal data. We argue that current studies related to the development of credit scoring models have largely ignored recent developments in statistical methods for sufficient dimension reduction. To contribute to the field of financial innovation, this study proposes a Dimension Reduction Assisted Credit Scoring (DRA-CS) method via distance covariance-based sufficient dimension reduction (DCOV-SDR) in Majorization-Minimization (MM) algorithm. First, in the presence of a large number of variables, the DRA-CS method results in greater dimension reduction and better prediction accuracy than the other methods used for dimension reduction. Second, when the DRA-CS method is employed with logistic regression, it outperforms existing methods based on different variable selection techniques. This study argues that the DRA-CS method should be used by financial institutions as a financial innovation tool to analyze high-dimensional customer datasets and improve the accuracy of existing credit scoring methods. [ABSTRACT FROM AUTHOR]

Purpose: The prospect of Sub-Saharan Africa (SSA) as an apparel-sourcing base for US fashion companies has been a growing heated debate among academia, industry practitioners and policymakers. This study aims to evaluate SSA countries' readiness to serve as an alternative sourcing destination to Asia for US fashion companies, focusing on comparing the similarities and differences of US apparel imports from these two regions at the product level. Design/methodology/approach: This study was based on a statistical analysis of detailed product features and assortment information of thousands of apparel items at the stock-keeping unit level sold by US retailers between January 2021 and December 2023. Findings: US fashion companies seemed to leverage SSA countries as suppliers of "niche products," such as those relatively simple and basic apparel categories containing African cultural elements and targeting the luxury and premium market segment. However, the range of apparel products available for US fashion companies to source from the SSA region remained significantly more limited than those from Asia. Also, US apparel imports from SSA countries were primarily made of cotton and polyester, with less use of other fiber types, including nylon, rayon, viscose, wool and those made from recycled textile materials. Originality/value: The study's findings provided fresh insights into why US fashion companies sourced from SSA countries and the specific types of products they were sourcing, going beyond existing studies based on macro trade statistics. The results also deepened the understanding of SSA countries' competitiveness as an apparel-sourcing destination and their potential to serve as an alternative to sourcing from Asia, particularly from a unique product perspective. [ABSTRACT FROM AUTHOR]

Purpose: This paper explores the association between a variety of trade and government policy measures of both home and trade partners introduced during the Covid-19 pandemic within the gravity-like framework by using the Turkish bilateral trade statistics at the six-digit product level from February 2020 to March 2022. Design/methodology/approach: The empirical model is estimated by employing the two-way fixed effects (TWFE) estimation technique to get rid of the effects of unobserved time-invariant idiosyncratic country-product effects in the model and to evade the possible autocorrelation problem of trade measures. Findings: Our empirical analysis suggests that lower Turkish GVC participation during this period can be attributed to lower mobilities and the lack of vaccines globally. Our analysis utilizing the different income groups of countries and technology group of sectors suggests that Turkish high-tech backward participation with developed countries can be more sensitive to any changes in Covid-19-related policy measures, whereas Turkish forward participation with both developed and developing markets can be more volatile during the pandemic because of the containment measures. Practical implications: Sustaining mobility in the global production process is the key finding to sustain both backward and forward GVC linkages. In addition, enhancing a variety of partner countries is crucial for sustaining the flows of imported intermediates of the Turkish manufacturing sectors. Moreover, the sophistication of Turkish exported products can be the solution to continue the forward GVC participation even in the shock times. Given the product and partner country-level heterogeneities regarding contingency measures implemented by the governments, policymakers should carefully monitor each sub-sample separately and focus especially on enhancement in information, communication and transportation infrastructures to mitigate the contagious effect of any external shocks. Originality/value: The unique monthly six-digit bilateral product-level trade dataset enables us to observe and utilize heterogeneous effects at the product, sector and partner country levels. [ABSTRACT FROM AUTHOR]

We consider the problem of pricing in a large market, which arises as a limit of small markets within which there are finitely many traded assets. We show that this framework allows accommodating both marginal-utility-based prices (for stochastic utilities) and arbitrage-free prices. Adopting a stochastic integration theory with respect to a sequence of semimartingales, we introduce the notion of marginal-utility-based prices for the large (post-limit) market and establish their existence, uniqueness and relation to arbitrage-free prices. These results rely on a theorem of independent interest on utility maximisation with a random endowment in a large market that we state and prove first. Further, we provide approximation results for the marginal-utility-based and arbitrage-free prices in the large market by those in small markets. In particular, our framework allows pricing asymptotically replicable claims, where we also show consistency in the pricing methodologies and provide positive examples. [ABSTRACT FROM AUTHOR]

Instrumental variables are commonly used to estimate treatment effects in cases of imperfect compliance. However, if participation in the program is misreported, standard techniques can yield severely biased results. We present a new command, ivreg2m, that implements the mismeasured robust local average treatment-effect estimator developed by Calvi, Lewbel, and Tommasi (2022, Journal of Business and Economic Statistics 40: 1701–1717) and Tommasi and Zhang (2024b, Journal of Applied Econometrics, https://doi.org/10.1002/jae.3079), to estimate the heterogeneous treatment effect of a program in the presence of treatment noncompliance and misreporting. The ivreg2m command can be used as the preferred strategy in cases of exogenous (nondifferential) misclassification. [ABSTRACT FROM AUTHOR]

We present a new disaggregated dataset on Germany's exports and imports from 1880 to 1913. The data covers trade in both quantity and value at the product level. It thus comprises the universe of the data in terms of products and trade partners, at annual frequency. To ensure comparability over time and to data from other countries, the historical product categories are re-classified according to the Standard International Trade Classification (SITC, Rev. 2 and Rev. 4). Moreover, we integrate Hamburg and Bremen into the German trade statistics – two important port cities, which were not covered by the major German trade records until 1888. We describe the new data in terms of five stylized facts and make suggestions for its use in research. [ABSTRACT FROM AUTHOR]

With the rapid development of the biomedical industry in various countries, the global trade of biomedical products is increasingly active. Based on the biomedical products trade data in the United Nations Commodity Trade Statistics Database from 2000 to 2020, social network analysis is applied to reveal the structural characteristics of the global biomedical products trade network and the evolution of China's position. The study shows that: ①The global biomedical products trade network is closely linked, with a relatively stable trade network structure and the characteristics of a "small world" network. ②The spatial distribution of nodes in the global biomedical products trade network is uneven, with high node degree countries located at the core of the network and little network heterogeneity. ③The core-edge structure of the global biomedical products trade network has changed from the "four-legged" situation formed by the US, Germany, France and Belgium to the "three-legged" situation formed by the US, France and Belgium. The community structure has evolved in a trend of fragmentation followed by restructuring. ④China's position in the global biomedical products trade network is gradually increasing, its role as a hub is obvious, and its dependence on the major global biomedical products trading countries is gradually decreasing. [ABSTRACT FROM AUTHOR]

Inside the generation of rapid technological development, rising industries are increasingly counting on big facts to pressure growth and innovation. This examine explores the transformative effect of huge facts generation on the analysis of commercial circulate statistics in those burgeoning sectors. By way of harnessing numerous and voluminous datasets, organisations inside rising industries can uncover crucial insights, optimise delivery chains, are expecting marketplace developments, and enhance patron reviews. The paper begins by using outlining the unique challenges and possibilities that emerging industries face within the virtual landscape, emphasising the want for strong facts-driven techniques. We delve into the methodologies of large facts analytics, together with data acquisition, garage, processing, and visualization strategies tailor-made for the nuanced requirements of these industries. A crucial examination of case research wherein huge facts has been efficaciously carried out offers realistic insights into its effectiveness and barriers. The examine similarly investigates the role of advanced analytics, system studying, and AI in refining data evaluation techniques, providing a complete view of the way those technologies synergistically make contributions to strategic selection-making. Moral issues, especially regarding records of privateness and safety, are also addressed, acknowledging the responsibilities that accompany the utilization of huge information. The paper concludes by projecting future traits in big statistics programs within rising industries, which includes the capacity for predictive analytics and the integration of IoT gadgets. This research now not only underscores the significance of big statistics in revolutionising business flow however also serves as a guiding framework for industry leaders and stakeholders trying to navigate the complexities of the digital age in rising markets. [ABSTRACT FROM AUTHOR]

Summary: Medeiros et al. (2021) (Journal of Business & Economic Statistics, 39:1, 98–119) find that random forest (RF) outperforms US inflation forecasting benchmarks. We replicate the main results in Medeiros et al. (2021) and (1) considerably expand the set of machine learning methods, (2) analyse the predictive ability of both the initial and extended sets of methods on Canadian and UK data, (3) add results on coverage rates and widths of prediction intervals and (4) extend the sample from January 2016 to October 2022. Our narrow replication confirms the main findings of the original paper. However, the wider replication results suggest that other methods are competitive with RF and often more accurate. In addition, RF produces disappointing results during the coronavirus pandemic and subsequent high inflation of 2020–2022, whereas a stochastic volatility model and some gradient boosting methods produce more accurate forecasts. [ABSTRACT FROM AUTHOR]

This paper explores the effects of industrial robots and information and communication technology (ICT) on regional employment in European Union countries. The empirical analysis relies on a harmonised comprehensive regional dataset that combines business statistics and national and regional accounts data. This rich dataset enables us to provide detailed insights into the employment effects of automation and computerisation in EU regions for the period 2001–16. The results suggest relatively weak effects on regional total employment dynamics. However, industrial robots show negative employment effects in local manufacturing industries and positive employment effects in local non-manufacturing industries. While the negative effect is concentrated in particular local manufacturing industries, the positive effect has operated in local service industries. Information technology investments show positive employment effects in local manufacturing industries and some individual local service industries, while communication technology investments are shown to be irrelevant for employment dynamics. In contrast, software and database investments have had a predominantly negative association with local employment. [ABSTRACT FROM AUTHOR]

Statistics knowledge, literacy, and skills are vital to undergraduates' academic and professional careers. Students' attitudes toward statistics play a vital role in their understanding of the importance of statistics. Therefore, it is important to examine their attitude toward statistics. This study investigated the attitude of 153 business students toward statistics and the factors that affect this attitude using the Survey Attitudes Toward Statistics (SATS‐36) scale. It also investigated the relationship between attitudinal components and students' demographic and academic characteristics. Five components underlying students' attitudes toward statistics were identified using exploratory factor analysis. Further, a background in quantitative subjects and demographic characteristics was found to affect students' attitudes toward statistics. The results suggested that statistics should be taught in a more practical way to relate it to students' lives, and teachers must adopt active learning methods in which students work with data and real‐life examples. [ABSTRACT FROM AUTHOR]

The photovoltaic (PV) industry experienced a drastic overcapacity, leading to heightened market competition in 2009. After the overcapacity, the EU and the USA initiated trade barriers targeting Chinese PV goods in 2011, stimulating China to seek alternative markets. In 2013, China announced the Belt and Road Initiative (BRI). Although the BRI is a framework of international cooperation instead of a specific strategy that aims at PV industry, its role in helping Chinese PV firms deal with the trade sanction should be addressed. Based on the data from Chinese Custom Trade Statistics spanning 2009 to 2016, this paper precisely quantifies the extent of PV trade deflection from China to BRI markets that can be ascribed to the BRI effects. Employing spatial analysis, we manifest the trade deflection on maps. Moreover, we adopt the linear probability models and panel logit models to investigate the impact of BRI on China's PV export expansion. The results show that BRI markets provide a buffer area for Chinese PV enterprises to alleviate their loss caused by trade sanctions. The BRI facilitated 4751 million more PV products conveyed from China to BRI markets from 2013 to 2016, occupying about 33.25% of the total export from China to BRI markets. Chinese PV firms prefer to explore the BRI markets than the rest of the world. Compared to state-owned enterprises (SOEs), non-SOEs are more active in entering BRI countries. However, no significant difference had been detected in BRI effects on SOEs and non-SOEs. It was noted that a robust local industrial basement related to the PV industry can potentially ease firms' transition to BRI markets. Comparatively, firms situated in central China have benefited more from the BRI than their counterparts in the coastal and western regions. Furthermore, the BRI encourages Chinese PV enterprises to venture into countries with low economic complexity. This paper provides novel insights into BRI by validating BRI's effects on Chinese PV firms' export expansion. On the one hand, unlike previous BRI research that rarely considers the unstable external environment, we validate that the BRI can provide a spare market for China when it competes with developed countries. On the other hand, by investigating the roles of firms, regions and destinations, our results are enlightening for renewable energy industrial and trade policymakers. [ABSTRACT FROM AUTHOR]

In this article, we consider how to make data more meaningful to students through the choice of data and the activities we use them in drawing upon students lived experiences more in the teaching of statistics and data science courses. In translating scholarship around culturally relevant pedagogy from the fields of education and mathematics education we develop the idea of culturally relevant data. We see this development as a key ingredient to implementing culturally relevant pedagogy in teaching data-intensive courses leveraging the centrality of context through data in both statistics and data science to engage students particularly from historically marginalized groups in STEM. We provide suggestions as to ways of finding or creating culturally relevant data and using them in implementing culturally relevant pedagogy to support the learning and flourishing of students in statistics and data science courses. We also present findings from pilot work we have done in implementing these data in statistics courses. Finally, we discuss lingering questions and possible next steps for research in this area. for this article are available online. [ABSTRACT FROM AUTHOR]

Copyright of Central Asian Economic Review is the property of Narxoz University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates the integration of social justice concepts using an introductory business statistics course at a Jesuit university, with a focus on poverty and income inequality. The study aims to enhance students' awareness, understanding, and empathy for social justice issues while strengthening their statistical knowledge. The pedagogical approach comprises three main elements: Twitter-based assignments centered on poverty statistics, a documentary on poverty in America, and a 5K race mission project. The results reveal a notable increase in poverty awareness, deepened empathy, and the development of moral and spiritual character among students. By combining knowledge, reflection, and action, this approach aligns with the Ignatian pedagogical paradigm and upholds the Jesuit mission of higher education. The study exemplifies fostering change by integrating social justice into higher education, encouraging students' active engagement as agents for positive impact, aligning with the principles for responsible management education set forth by the United Nations. [ABSTRACT FROM AUTHOR]

Summary Peter J. Rousseeuw is a statistician known mainly for his work on robust statistics and cluster analysis. Among his creations are least trimmed squares regression, the minimum covariance determinant estimator, the partitioning around medoids clustering method and the silhouettes graphical display. Peter obtained his PhD in 1981 following research carried out at the ETH in Zürich, Switzerland, which led to a book on influence functions. Later, he was a professor at Delft University of Technology, The Netherlands, and at the University of Antwerp, Belgium. Next, he was a researcher at Renaissance Technologies in New York for over a decade. He then returned to Belgium as a full professor at KU Leuven, until becoming emeritus in 2022. He is an elected member of the International Statistical Institute and a fellow of the Institute of Mathematical Statistics and the American Statistical Association. In the course of his career, Peter published three books and over 200 papers, together receiving over 100 000 citations. He was awarded the George Box Medal for Business and Industrial Statistics, the Research Medal of the International Federation of Classification Societies, the Frank Wilcoxon Prize, and twice the Jack Youden Prize. Recently, Peter received the 2024 ASA Noether Distinguished Scholar Award for nonparametric statistics. His former PhD students include Annick Leroy, Rik Lopuhaä, Geert Molenberghs, Christophe Croux, Mia Hubert, Stefan Van Aelst, Tim Verdonck and Jakob Raymaekers. He is the creator and sole sponsor of the Rousseeuw Prize for Statistics, which was first handed out by the King of Belgium in 2022. [ABSTRACT FROM AUTHOR]

The research aimed to analyze the status of Egyptian agricultural exports of the most important vegetable crops to the European market through a competitive study of Egypt’s most important agricultural exports of potatoes and onions in the European Union markets. The research relied on the method of descriptive and quantitative analysis through various economic methods and models, relying on data from the Food and Agriculture Organization, Trade statistics website for international business development Trade map. The results of the study showed that by examining the relative importance of the value of Egyptian exports of the most important vegetable crops, the potato crop came in first place in terms of the value of Egyptian vegetable exports to European markets as an average for the period 2019-2022, which amounted to about 261 million dollars, representing about 47.74% of the average value of the total value of vegetable exports. Which amounted to about $547.21 million. While the onion crop came in second place with an average of about 223.38 million dollars, representing 40.82%, while the crops of garlic, peas, tomatoes, and eggplant came in third to sixth place with an average of about 3.82, 1.68, 1.19, and 0.85 million dollars, with a relative importance amounting to About 0.69%, 0.31%, 0.22%, 0.15% of the average total value of vegetable exports during the study period. [ABSTRACT FROM AUTHOR]

There is a growing demand for statistics to better understand the globalisation that is accelerating due to the removal of barriers in international trade and to technological progress. Key players in globalisation are the multinational enterprise (MNE) groups that have increased in number and complexity and need to be properly represented by macroeconomic and business statistics. To deal with this need, the European Union Member States, the European Free Trade Association (EFTA) countries and Eurostat have collaborated to create the EuroGroups Register (EGR). This paper explores the use of web intelligence to improve the accuracy and completeness of the EGR, which makes use of tools for extracting and exploring information from the World Wide Web. Additionally, it presents a methodology to assess the quality of the information retrieved from the web, based on an ex-post comparison with the official information contained in the EGR. The results provide indications about the possibility of using web intelligence tools to support MNE group monitoring and to complete the EGR data in cases where this information is missing. Finally, it dives deeply into the approach to harness Wikipedia as a data source and into the techniques and methodology used to extract data from this source. [ABSTRACT FROM AUTHOR]

Copyright of Contaduría y Administración is the property of Facultad de Contaduria y Administracion-Universidad Nacional Autonoma de Mexico and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the early twentieth century, one could find kimono-like garments of various kinds in Britain: kimono made originally for Japanese consumers, contemporary robes made in Japan expressly for export, and kimono-inspired clothing made domestically. The present essay probes the merchandising of these new products in Britain, examining both British consumer tendencies and Japanese marketing practices, through analysis of Japanese trade statistics and reports on the international expositions of the day. Ultimately, this paper argues that kimono played a role, both literally and figuratively, in staging increased social justice for British women in the twentieth century, offering at least occasional relief from the debilitating fashions of the period, all of which required a corset, and providing a metaphor for a less restrictive social environment. While other scholars have hinted at such associations, this paper provides evidence, through analysis of contemporaneous journalism and advertising, that the women who wore these garments explicitly made such connections. More than an artistic stimulus of japonisme , the kimono and the fashions it subsequently inspired became symbols of the women's suffrage movement in early twentieth-century Britain. [ABSTRACT FROM AUTHOR]

Sino-Latin American trade have increased substantially for decades. However, the increasing trade imbalances between China and several Latin American countries have yielded growing trade frictions. In this paper, we re-calculate the bilateral trade balance using the value-added content in trade and measure the contribution of Sino-Latin American trade to economic growth. The results show that gross trade statistics seriously overestimate the real bilateral trade imbalance, and China's trade surplus with Mexico decreased by over 50%. The composition of Sino-Latin American trade suggests that most Latin American economies are complementary with Chinese economy. The South-South trade has been increasingly important for both China's and Latin America's economic development. We foresee an optimistic future of relations and continuously increasing trade between China and Latin America. Meanwhile, more efforts should be made to settle trade frictions and intensify Sino-Latin American relations. [ABSTRACT FROM AUTHOR]

Nature loss threatens businesses, the global economy and financial stability. Understanding and addressing these risks for business will require credible measurement approaches and data. This paper explores how natural capital accounting (NCA) can support business data and information needs related to nature, including disclosures aligned with the Taskforce on Nature-related Financial Disclosures recommendations. As businesses seek to measure, manage and disclose their nature-related risks and opportunities, they will need well-organized, consistent and high-quality information regarding their dependencies and impacts on nature, which few businesses currently collect or track in-house. NCA may be useful for these purposes but has not been widely used or applied by businesses. National NCA guided by the U.N. System of Environmental-Economic Accounting may provide: (i) a useful framework for businesses in conceptualizing, organizing and managing nature-related data and statistics; and (ii) data and information that can directly support business disclosures, corporate NCA and other business applications. This paper explores these opportunities as well as synergies between national and corporate natural capital accounts. In addition, the paper discusses key barriers to advancing the wider use and benefits of NCA for business, including: awareness of NCA, data access, business capabilities related to NCA, spatial and temporal scales of data, audit and assurance considerations, potential risks, and costs and incentives. This article is part of the theme issue 'Bringing nature into decision-making'. [ABSTRACT FROM AUTHOR]

In marketing, the traditional approach for teaching quantitative concepts uses individual courses, such as marketing research and business statistics. While this pedagogy is common, research indicates it is difficult for students to master quantitative concepts in a single course. Instead, quantitative reasoning (QR) requires repeated practice, so students develop a quantitative habit of mind. While the integration of analytics across the marketing curriculum could help students deepen their understanding of quantitative concepts, program-wide curricular change tends to be slow because it requires the coordination of multiple instructors across several courses. The current study investigated an innovative alternative – spiral pedagogy – that integrated quantitative concepts into a marketing management course. The study analyzed 384 QR assessments from 40 students before, during, and after the course. The time of the assessment had a large effect on students' use of QR (ηp2 =.93) and quantitative confidence (ηp2 =.89). Qualitative feedback also indicated the spiraled lessons fostered a quantitative habit of mind that helped students beyond the classroom. [ABSTRACT FROM AUTHOR]

Although Hong Kong's entrepôt role in China's foreign trade has long received scholarly attention, there is a relative paucity of studies on China's indirect exports via Hong Kong in the Mao era (1949–1976). Using data from Chinese and Hong Kong trade statistics, this study estimates China's indirect exports via Hong Kong in the 1950s and calculates their share in China's export trade. The principal findings are threefold. First, their value, destination, and commodity composition changed markedly. Second, their share in China's export trade declined during the decade, but the speed and extent of the decline varied by destination. In particular, their share in China's exports to the non-Communist world initially increased from 1950 to 1952 and subsequently decreased. Third, their share in China's exports to the non-Communist world was slightly higher in the 1950s than in the 1980s. These findings support recent historical studies indicating that Hong Kong played an essential role in selling Chinese goods to non-Communist countries during the early Mao era. Fundamentally, this study modifies the widely held view that Hong Kong's entrepôt role in China's foreign trade was much less important in the Mao era than in the post-1978 reform era. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the predictive content of 3 new business condition indexes and uncertainty measures that are estimated using high-frequency financial and low-frequency macroeconomic time series data. More specifically, our measures are defined as latent factors that are extracted from a state space model that includes multiple different frequencies of non-parametrically estimated components of quadratic variation, as well as mixed frequency macroeconomic variables. When forecasting growth rates of various monthly financial and macroeconomic variables, use of our new mixed frequency factors is shown to result in significant improvement in predictive performance, relative to a number of benchmark models. Additionally, when used to forecast corporate yields, predictive gains associated with the use of our measures are shown to be monotonically increasing, as one moves from predicting higher to lower rated bonds. This is consistent with the existence of a natural pricing channel wherein financial risk (as measured using our volatility factors) contains more predictive information for lower grade bonds. We also find that a variety of extant risk factors including the Aruoba et al. [(2009a). Real-time measurement of business conditions. Journal of Business & Economic Statistics , 27 (4), 417427] business conditions index also contain marginal predictive content for the variables that we examine, although their inclusion does not reduce the usefulness of our measures. [ABSTRACT FROM AUTHOR]

The article "Average annual change by firm size in the EU27, 2009-2023" analyzes the productivity changes in EU countries over the period 2009-2023. It highlights the growth in the total number of enterprises, with Ireland leading in significant growth driven by large multinational companies. The article also discusses the challenges faced by countries like Luxembourg, Austria, and Greece in maintaining productivity levels, with Greece showing effectively stagnant growth. The data is presented based on enterprise size categories, emphasizing the importance of productivity for social welfare and economic development. [Extracted from the article]

As the COVID-19 pandemic took hold in early months of 2020, education at all levels was pushed to emergency fully remote, online formats. This emergency shift affected all aspects of teaching and learning with very little notice and often with limited resources. Educators were required to convert entire courses online and shift to remote instructional approaches practically overnight. Students found themselves enrolled in online courses without choice and struggling to adjust to their new learning environments. This article highlights some of the challenges and successes of teaching emergency online undergraduate statistics courses. In particular, we discuss challenges and successes related to (a) technology, (b) classroom community and feedback, and (c) student-content engagement. We also reflect on the opportunity to continue to enhance and enrich the learning experiences of our students by using some of the lessons learned from emergency online teaching as new permanent online statistics courses are developed and/or moved back into the classroom. [ABSTRACT FROM AUTHOR]

Many approaches exist in teaching statistics, however, learning statistics is frequently perceived by students as challenging. While evidence-based teaching approaches like case discussions and flipped-classroom models have been successfully incorporated into multiple disciplines, these methods can have inadequate success when students are affectively detached or sometimes paralyzed by the subject. The sudden need to switch to virtual teaching in Spring 2020 created unprecedented challenges that were overwhelming for instructors and students. This article explores the challenges of teaching a statistics course, and the importance of purposeful use of technology and supportive feedback to effectively engage students and promote learning. Furthermore, this article discusses the results of implementing a Technology-enhanced Supportive Instruction (TSI) model (used since Fall 2019) in teaching a business statistics course. The TSI model uses Microsoft Excel in conjunction with intentional interactions between the student and teacher to help students stay engaged in face-to-face and remote learning. The analysis of Fall 2020 course evaluations, when the course was taught in a dual mode, shows positive students' perception of instruction and engagement, and indicates that Fall 2020 ratings during dual-mode instruction were not different from Fall 2019 ratings during face-to-face instruction, which supports the potential utility of the model. for this article are available online. [ABSTRACT FROM AUTHOR]

Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is unconventional to many students in a second or third course in statistics or data science. As a result, this topic is often not as intuitive to students as some of the more traditional statistical modeling tools. In order to facilitate students' mastery of the topic and promote active learning, we developed some in-class activities and their accompanying Shiny applications for teaching support vector classifiers. The designed course materials aim at engaging students through group work and solidifying students' understanding of the algorithm via hands-on explorations. The Shiny applications offer interactive demonstration of the changes of the components of a support vector classifier when altering its determining parameters. With the goal of benefiting the broader statistics and data science education community, we have made the developed Shiny applications publicly available. In addition, a detailed in-class activity worksheet and a real data example are also provided in the online . [ABSTRACT FROM AUTHOR]

Implementing approaches based on process mining in inter-organizational collaboration environments presents challenges related to the granularity of event logs, the privacy and autonomy of business processes, and the alignment of event data generated in inter-organizational business process (IOBP) execution. Therefore, this paper proposes a complete and modular data-driven approach that implements natural language processing techniques, text similarity, and process mining techniques (discovery and conformance checking) through a set of methods and formal rules that enable analysis of the data contained in the event logs and the intra-organizational process models of the participants in the collaboration, to identify patterns that allow the discovery of the process choreography. The approach enables merging the event logs of the inter-organizational collaboration participants from the identified message interactions, enabling the automatic construction of an IOBP model. The proposed approach was evaluated using four real-life and two artificial event logs. In discovering the choreography process, average values of 0.86, 0.89, and 0.86 were obtained for relationship precision, relation recall, and relationship F-score metrics. In evaluating the quality of the built IOBP models, values of 0.95 and 1.00 were achieved for the precision and recall metrics, respectively. The performance obtained in the different scenarios is encouraging, demonstrating the ability of the approach to discover the process choreography and the construction of business process models in inter-organizational environments. [ABSTRACT FROM AUTHOR]

We estimate the policy effect of belt and road initiative (BRI) on China's import. We use highly detailed, product‐country level data from China Customs Trade Statistics Database which covers 5364 types of products and 201 economies from 2010 to 2017 to investigate the policy effects and the underlying mechanisms. We find that the BRI has significantly boosted China's imports from countries along the route. The BRI have significant expansion effect on imports of existing products as well as positive effects on imports of new product import and product exit, which means BRI may also contribute to the optimization of product structure and growth in value of China's imports. Moreover, BRI have positive effects on both general trade and processing trade. Mechanism analysis shows that policy coordination, cultural affinity, and maritime transportation connectivity have strong explanatory power on the policy effects of BRI. We also find that the BRI has played a "boosting" role for import boost for new RTA partner as well as non‐RTA members along the route. We further examine the impact of BRI on global bilateral trade, and the result shows that BRI has generally promoted bilateral trade among all countries along the route, not limited to origin‐China pairs. [ABSTRACT FROM AUTHOR]

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Indonesia is one of the countries exporting caught, cultivated, and frozen shrimp to both developed and developing countries. Previously, the frozen shrimp export data was categorized based on the countries it was exported to. However, the list of frozen shrimp exports did not include the export levels. This research aims to explore the application of machine learning to analyze the frozen shrimp export data from the main destination countries using the K-Medoids cluster. The data for this research was sourced from the Bulletin of Foreign Trade Export Statistics documents by Product Group and Country, January 2023. The data used in this study covers the period from 2022 to 2023 and includes research variables such as net weight of goods (in tons) and FOB value (free on board). The cluster analysis revealed the formation of two clusters: one with high export levels and another with medium and low export levels. [ABSTRACT FROM AUTHOR]

Teaching statistics to undergraduate business students is an enduring challenge, often due to student apprehension and low understanding of relevance. The Data to Decision Project is an approach designed to address this challenge. At the onset of an introductory statistics course, fifty-two students received instruction on the Define, Collect, Organize, Visualize and Analyze (DCOVA) framework to integrate data with business-related decision-making. Concurrent with this instruction, students selected and completed individual projects, using the DCOVA framework to explore their individual decisions. Quantitative and qualitative methods indicated that this approach positively impacted the students' ability to apply statistics, as well as their attitudes regarding their statistics abilities. [ABSTRACT FROM AUTHOR]

This paper proposes a jackknife Lagrange multiplier (JLM) test for instrumental variable regression models, which is robust to (i) many instruments, where the number of instruments may increase proportionally with the sample size, (ii) arbitrarily weak instruments, and (iii) heteroskedastic errors. In contrast to Crudu, Mellace, and Sándor (2021, Econometric Theory 37, 281–310) and Mikusheva and Sun (2021, Review of Economic Studies 89, 2663–2686), who proposed jackknife Anderson–Rubin tests that are also robust to (i)–(iii), we modify a score statistic by jackknifing and construct its heteroskedasticity robust variance estimator. Compared to the Lagrange multiplier tests by Kleibergen (2002, Econometrica 70, 1781–1803) and Moreira (2001, Tests with Correct Size when Instruments Can Be Arbitrarily Weak , Working paper) and their modification for many instruments by Hansen, Hausman, and Newey (2008, Journal of Business & Economic Statistics 26, 398–422), our JLM test is robust to heteroskedastic errors and may circumvent a possible decrease in the power function. Simulation results illustrate the desirable size and power properties of the proposed method. [ABSTRACT FROM AUTHOR]

Figures used in statistics and other sciences play a vital role in understanding and analyzing the problems under study. Due to the complexity and diversity of these problems, figures such as cartograms, choropleth maps, or radar charts take various geometric forms. Their visual evaluation from the view of geometric similarity is essential but insufficient. This paper proposes and theoretically justifies new metrics based on graph theory. They make it possible to quickly determine the degree of similarity of the statistical figures used in the research procedure. The new metrics were used to 1. Determine the similarity of the domestic route networks of major U.S. airlines, 2. Determine the similarity of the distribution of votes cast in U.S. presidential election in each state in 2016 and 2020, 3. Compare radar charts of some countries, constructed based on the Global Competitiveness Index, 4. Analyze the similarity of neutrosophic double line graphs representing sets of approximate (neutrosophic) numbers. This improves analytical capabilities concerning various processes mapped with well-known types of statistical charts, such as choropleth maps, radar charts, etc. [ABSTRACT FROM AUTHOR]

Koopman et al. (2014) and Los et al. (2016) decomposed gross exports into various value-added components by adopting the input–output assumption of disconnection between production and final demand. Such an assumption, however, neglects the ability of production inflows to generate income and consumption, and therefore additional impacts on production. To achieve a more complete understanding of the role played by trade, this article presents a method for quantifying the value added of exports that reflects the linkages between production and private consumption. In the tradition of Miyazawa (1968, 1976) and Sonis and Hewings (1973), the proposed model endogenously defines household consumption in the output determination, thus improving the way in which the interdependencies between income and output generation processes are revealed. The proposal is directly applicable empirically through available world trade databases. [ABSTRACT FROM AUTHOR]

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Learning statistics can be challenging for many students, due to their inability to engage in statistical reasoning and application of techniques. This challenge becomes compounded in online learning contexts where students are spatially and temporally separated from the teacher. This paper describes and explains a case of theory-driven interventions designed to enhance the learning experiences of students enrolled in two similar business statistics units, one for undergraduate and the other for postgraduate programs. The paper based its claims primarily on the analysis of data from a student evaluation of teaching survey. This study affirmed the importance of a pedagogy-first approach. It argued that the interventions, which were effective in enhancing the student learning experience, were underpinned by a robust pedagogical analysis of the teaching and learning issues using both constructive alignment and transactional distance theory lenses. [ABSTRACT FROM AUTHOR]

Combining cross-sectional and time-series data is a long and well-established practice in empirical economics. We develop a central limit theory that explicitly accounts for possible dependence between the two datasets. We focus on common factors as the mechanism behind this dependence. Using our central limit theorem (CLT), we establish the asymptotic properties of parameter estimates of a general class of models based on a combination of cross-sectional and time-series data, recognizing the interdependence between the two data sources in the presence of aggregate shocks. Despite the complicated nature of the analysis required to formulate the joint CLT, it is straightforward to implement the resulting parameter limiting distributions due to a formal similarity of our approximations with Murphy and Topel's (1985, Journal of Business and Economic Statistics 3, 370–379) formula. [ABSTRACT FROM AUTHOR]

In this article, we explore the drivers of students' success in a first-year university statistics course. Using a unique sample from Westminster International University in Tashkent, we discover that student engagement with their studies is reflected in their class attendance and in the use of online resources, which continue to play an important role in their academic performance. Moreover, we discovered that socio-demographic factors such as age, gender, marital status, employment, and language of instruction of prior schooling are significant in explaining student academic outcomes. These factors have important policy implications for both the university and the broader national level. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic altered course delivery in higher education at many universities. This article evaluates the differences between student experiences in the fall 2019 semester (pre-pandemic) and those during the fall 2020 semester (pandemic) within a multidisciplinary, project-based introductory statistics course. Results indicated that there were minimal differences in student experiences of this course based on delivery mode (in person vs. online). [ABSTRACT FROM AUTHOR]