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Inspection authorities such as the Port State Control Memoranda of Understanding use different policies and targeting methods to select vessels for inspections and rely primarily on past inspection outcomes. One of the main goals of inspections is to improve the safety quality of vessels and to reduce the probability of future incidents. This study shows there is room for improvement in targeting vessels for inspections and in determining vessel-specific inspection priority areas (e.g., bridge management versus machinery related items). For the year 2018, sixty percent of vessels that experienced very serious or serious (VSS) incidents were not selected for inspection up to three months prior to the incident and forty percent of the vessels that were inspected still had incidents of which only four percent were detained. Furthermore, one can observe a very low correlation (−0.04) between the probabilities of detention and incidents (VSS) for the year 2018. The proposed approach treats detention and incident types as separate risk dimensions and evaluates seven targeting methods against random selection of vessels using empirical data for 2018. The analysis is based on three comprehensive data sets that cover the world fleet and shows potential gains (reduction of false negative events) of 14–27 percent compared to random selection. This can be further improved by adding eight inspection priority risk areas that help inspectors to focus inspections by providing insight in the individual risk profile of vessels. Policy makers can further customize the approach by classifying the risk of vessels into categories and by selecting inspection targets and benchmark samples. A small application example is provided to demonstrate feasibility of the proposed approach for policy makers and inspection authorities.

This empirical study examines distance effects on cross-border electronic commerce and in particular the importance of express delivery in reducing the time dimension of distance. E-commerce provides suppliers with a range of opportunities to reduce distance as perceived by online buyers. They can reduce psychological barriers to cross-border demand by designing websites that simplify the search for and comparison of products and suppliers across countries. They can reduce cost barriers by applying pricing strategies that redistribute transportation costs, and they can overcome time barriers offering express delivery services. This study of 721 regions in five countries of the European Union shows that distance is not \"dead\" in e-commerce, that express delivery reduces distance for cross-border demand, and that e-demand delivered by express services is more time sensitive and less price sensitive than e-demand satisfied by standard delivery. The willingness of e-customers to pay for express services is shown to be affected by income and by the relative lead-time benefits and express charges. Furthermore, the adoption of express delivery is positively associated with e-loyalty in terms of repurchase rates. The results confirm the importance for e-suppliers of cleverly designed delivery services to reduce distance in order to attract online customers across borders.

Nowadays applied work in business and economics requires a solid understanding of econometric methods to support decision-making. Combining a solid exposition of econometric methods with an application-oriented approach, this rigorous textbook provides students with a working understanding and hands-on experience of current econometrics. Taking a 'learning by doing' approach, it covers basic econometric methods (statistics, simple and multiple regression, nonlinear regression, maximum likelihood, and generalized method of moments), and addresses the creative process of model building with due attention to diagnostic testing and model improvement. Its last part is devoted to two major application areas: the econometrics of choice data (logit and probit, multinomial and ordered choice, truncated and censored data, and duration data) and the econometrics of time series data (univariate time series, trends, volatility, vector autoregressions, and a brief discussion of SUR models, panel data, and simultaneous equations). · Real-world text examples and practical exercise questions stimulate active learning and show how econometrics can solve practical questions in modern business and economic management. · Focuses on the core of econometrics, regression, and covers two major advanced topics, choice data with applications in marketing and micro-economics, and time series data with applications in finance and macro-economics. · Learning-support features include concise, manageable sections of text, frequent cross-references to related and background material, summaries, computational schemes, keyword lists, suggested further reading, exercise sets, and online data sets and solutions. · Derivations and theory exercises are clearly marked for students in advanced courses. This textbook is perfect for advanced undergraduate students, new graduate students, and applied researchers in econometrics, business, and economics, and for researchers in other fields that draw on modern applied econometrics.

Purpose The purpose of this paper is to show that intentional demand forecast bias can improve warehouse capacity planning and labour efficiency. It presents an empirical methodology to detect and implement forecast bias. Design/methodology/approach A forecast model integrates historical demand information and expert forecasts to support active bias management. A non-linear relationship between labour productivity and forecast bias is employed to optimise efficiency. The business analytic methods are illustrated by a case study in a consumer electronics warehouse, supplemented by a survey among 30 warehouses. Findings Results indicate that warehouse management systematically over-forecasts order sizes. The case study shows that optimal bias for picking and loading is 30-70 per cent with efficiency gains of 5-10 per cent, whereas the labour-intensive packing stage does not benefit from bias. The survey results confirm productivity effects of forecast bias. Research limitations/implications Warehouse managers can apply the methodology in their own situation if they systematically register demand forecasts, actual order sizes and labour productivity per warehouse stage. Application is illustrated for a single warehouse, and studies for alternative product categories and labour processes are of interest. Practical implications Intentional forecast bias can lead to smoother workflows in warehouses and thus result in higher labour efficiency. Required data include historical data on demand forecasts, order sizes and labour productivity. Implementation depends on labour hiring strategies and cost structures. Originality/value Operational data support evidence-based warehouse labour management. The case study validates earlier conceptual studies based on artificial data.

This book has grown out of half a century of experience of teaching undergraduate econometrics at the Econometric Institute in Rotterdam. It combines a solid exposition of econometric methods with the application-oriented approach that is characteristic of the Rotterdam tradition in econometrics. Covering basic econometric methods (statistics, simple and multiple regression, nonlinear regression, maximum likelihood, generalized method of moments), this books explains their practical applications in modern business and economics and provides examples of these. Much attention is paid to the creative process of model building, with due attention for diagnostic testing and model improvement. The last part of the book is devoted to two major application areas: the econometrics of choice data (logit and probit, multinomial and ordered choice, truncated and censored data, duration data) and the econometrics of time series data (univariate time series, trends, volatility, vector autoregressions, and a brief discussion of SUR models, panel data, and simultaneous equations). In addition, data sets and (for instructors) full solutions of all exercises are available to readers. The book provides a thorough training in modern applications of econometrics to practical questions in business and economics. It is guided by a spirit of practical learning, and provides a wealth of examples and practical exercises based on a wide variety of data sets drawn from business and micro, macro, and international economics. The book will be suitable for introductory applied econometrics courses at undergraduate level up to more advanced courses at the graduate level.

A new method of leading index construction is proposed, which explicitly takes into account the purpose of using the index for forecasting a coincident economic indicator. This so-called principal covariate index combines the need for compressing the information in a large number of individual leading indicator variables with the objective of forecasting. In an empirical application to forecast future growth rates of the Conference Board’s Composite Coincident Index and its constituents, the forecasts of the principal covariate index are more accurate than those obtained either from the Composite Leading Index of the Conference Board or from an alternative index-based on principal components. JEL Classification: C32, C53, E27 Keywords: index construction, business cycles, principal component, principal covariate, time series forecasting, variable selection

The problem of modeling a finite time series by means of a linear shift invariant system will be considered. To analyze this problem the concepts of complexity of a model and corroboration of a model by data are introduced. Various properties of data modeling procedures are defined. These properties are investigated for the partial realization procedure. An alternative procedure will be described which takes into account the concept of corroboration. It turns out that this procedure has many desirable properties.

A new method of leading index construction is proposed, which explicitly takes into account the purpose of using the index for forecasting a coincident economic indicator. This so-called principal covariate index combines the need for compressing the information in a large number of individual leading indicator variables with the objective of forecasting. In an empirical application to forecast future growth rates of the Conference Board's Composite Coincident Index and its constituents, the forecasts of the principal covariate index are more accurate than those obtained either from the Composite Leading Index of the Conference Board or from an alternative index-based on principal components. [PUBLICATION ABSTRACT]