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The purpose of this paper is to explore the economic performance of Norwegian crop farms using a stochastic frontier analysis.

The analysis was based on a translog cost function and unbalanced farm-level panel data for 1991–2013 from 455 Norwegian farms specialized in crop production in eastern and central regions of Norway.

The results of the analysis show that the mean efficiency was about 78–81 percent. Farm management practices and socioeconomic factors were shown to significantly affect the economic performance of Norwegian crop farms.

Farmers are getting different types of support from the government and the study does not account for the different effects of different kinds of subsidy on cost efficiency. Different subsidies might have different effects on farm performance. To get more informative and useful results, it would be necessary to repeat the analysis with less aggregated data on subsidy payments.

One implication for farmers (and their advisers) is that many of them are less efficient than the estimated benchmark (best performing farms). Thus, those lagging behind the best performing farms need to look at the way they are operating and to seek out ways to save costs or increase crop production. Perhaps there are things for lagging farmers to learn from their more productive farming neighbors. For instance, those farmers not practicing crop rotation might be well advised to try that practice.

For both taxpayers and consumers, one implication is that the contributions they pay that go to subsidize farmers appear to bring some benefits in terms of more efficient production that, in turn, increase the supply of some foods so possibly making food prices more affordable.

Unlike previous performance studies in the literature, the authors estimated farm-level economic performance accounting for the contribution of both an important farm management practice and selected socioeconomic factors. Good farm management practices, captured through crop rotation, land tenure, government support and off-farm activities were found to have made a positive and statistically significant contribution to reducing the cost of production on crop-producing farms in the Central and Eastern regions of Norway.

This study aims to consider active vision in low-visibility environments to reveal the factors of optical properties which affect visibility and to explore a method of obtaining different depths of fields by multimode imaging.Bad weather affects the driver’s visual range tremendously and thus has a serious impact on transport safety.

A new mechanism and a core algorithm for obtaining an excellent large field-depth image which can be used to aid safe driving is designed and implemented. In this mechanism, atmospheric extinction principle and field expansion system are researched as the basis, followed by image registration and fusion algorithm for the Infrared Extended Depth of Field (IR-EDOF) sensor.

The experimental results show that the idea we propose can work well to expand the field depth in a low-visibility road environment as a new aided safety-driving sensor.

The paper presents a new kind of active optical extension, as well as enhanced driving aids, which is an effective solution to the problem of weakening of visual ability. It is a practical engineering sensor scheme for safety driving in low-visibility road environments.

This study seeks to examine the extent to which strands of inflationary related conditions (inflation expectations, inflation uncertainty and realized inflation); macroeconomic uncertainty and the likelihood of recessionary conditions influence performance indicators in the US banking sector over a specified time period.

The study adopts seemingly unrelated regression model (SUR) advanced by Zellner (1962) in its examination of how specific strands of inflationary conditions, and other adverse macroeconomic conditions influence performance dynamics in the US banking sector.

Empirical evidence suggest that among various adverse macroeconomic conditions examined, inflation expectations and macroeconomic uncertainty tend to have significant constraining impact on key performance indicators in the US banking sector than other conditions examined. Comparatively, this study finds that inflation expectations and macroeconomic uncertainty tend to have much more constraining impact on return on equity, than on return on assets in the US banking sector. Results further suggest that among the three bank performance indicators examined, net interest margin is the least vulnerable bank performance indicator to various adverse macroeconomic conditions examined in the study.

Apart from the various empirical results noted above, this study's findings are projected to help inform strategic planning decisions among institutions in the banking sector. The various findings could, for instance, inform policies and operational strategies geared toward reducing vulnerability associated with specific performance indicators such as return on equity. This reduction could be achieved by critically examining how the various performance indicators react to individual adverse macroeconomic conditions examined in this study. The process could ultimately help in developing tailored measures/procedures aimed at reducing how susceptible key performance indicators are to the various adverse macroeconomic conditions. This study's findings could also provide the platform for more adaptive policies aimed at minimizing the effects of noted macroeconomic conditions on operational efficiency in the banking sector.

The uniqueness of this study, compared to related ones found in the literature, stems from its treatment of three variant of related strands of macroeconomic condition (different variant of inflationary conditions) in the same framework in its empirical analysis.

本研究旨在探討與通貨膨脹有關的狀況的組成部分(通脹預期 、通脹不確定性及體現了的通脹), 宏觀經濟不確定性及經濟衰退狀況的可能性、在一段特定時間內對美國銀行業的表現指數有何種程度的影響。

研究採用塞爾納 (Zellner) (1962) 提出的看似無關迴歸模型 (SUR)，去探討通脹狀況的特定組成部分及其它不利的宏觀經濟狀況如何影響美國銀行業內的績效動態。

實證證據暗示在被研究的各個不利宏觀經濟狀況中，通脹預期及宏觀經濟不確定性，對美國銀行業內的主要業績指標的約束影響, 與其它被探討的狀況相比，往往會較重大。相對地、本研究結果顯示通脹預期及宏觀經濟不確定性，對美國銀行業資本回報率的約束影響、往往遠多於資產收益率。研究結果進一步顯示，在被探討的三個銀行業績指標中，就本研究所探討的各個不利的宏觀經濟狀況而言，淨息差是脆弱性最小的銀行業績指標。

除了上述各實證結果外，本研究結果預期會給銀行業內機構間作戰略規劃的決定時提供資料，譬如，各項研究結果或可在制定旨在減少與特定業績指標如資本回報率相聯繫的脆弱性的政策和經營策略時提供資料。這脆弱性的減少，是透過嚴謹地研究各個業績指標，如何對在本研究中被探討的個別不利宏觀經濟狀況作出反應而達致的。這程序或許最終會幫助建立一個以減少各個不利宏觀經濟狀況對主要業績指標的影響為目的的量身定制措施／程序。本研究的結果，或許亦可為更多旨在減弱眾所周知的宏觀經濟狀況對銀行業運營效率的影響的適應性政策提供平台。

與文獻中可見的相關研究比較，本研究的獨特性源於其實證分析，是涉及在同一個構架內處理宏觀經濟狀況相互有關的組成部分的三個變體 (通脹狀況的不同變體) 。

This paper considers locating congested fast charging stations (FCSs) and deploying chargers in a stochastic environment, while the related studies have predominantly focused on problems in deterministic environments. Reducing the inconvenience caused by congestion at FCSs is an important challenge for FCS service provider. This is the underlying motivation for this study to consider a problem for FCS network design with the congestion restriction in a stochastic environment. We proposed a maximal coverage problem subject to budget constraints and a congestion restriction in order to maximize the demand coverage. With the derivation of the congestion restriction in the considered stochastic environment, the problem is formulated into an integer programming model. A real-life case study is conducted and managerial implications are drawn from its results.

The article aims to establish whether the degree of aversion to inflation and the responsiveness to deviations from potential output have changed over time.

This paper assesses time variation in monetary policy rules by applying a time-varying parameter generalised methods of moments (TVP-GMM) framework.

Using monthly data until December 2022 for five inflation targeting countries (the UK, Canada, Australia, New Zealand, Sweden) and five countries with alternative monetary regimes (the US, Japan, Denmark, the Euro Area, Switzerland), we find that monetary policy has become more averse to inflation and more responsive to the output gap in both sets of countries over time. In particular, there has been a clear shift in inflation targeting countries towards a more hawkish stance on inflation since the adoption of this regime and a greater response to both inflation and the output gap in most countries after the global financial crisis, which indicates a stronger reliance on monetary rules to stabilise the economy in recent years. It also appears that inflation targeting countries pay greater attention to the exchange rate pass-through channel when setting interest rates. Finally, monetary surprises do not seem to be an important determinant of the evolution over time of the Taylor rule parameters, which suggests a high degree of monetary policy transparency in the countries under examination.

It provides new evidence on changes over time in monetary policy rules.

The paper compares multi-period forecasting performances by direct and iterated method using Bayesian vector autoregressive (VAR) models.

The paper adopts Bayesian VAR models with three different priors – independent Normal-Wishart prior, the Minnesota prior and the stochastic search variable selection (SSVS). Monte Carlo simulations are conducted to compare forecasting performances. An empirical study using US macroeconomic data are shown as an illustration.

In theory direct forecasts are more efficient asymptotically and more robust to model misspecification than iterated forecasts, and iterated forecasts tend to bias but more efficient if the one-period ahead model is correctly specified. From the results of the Monte Carlo simulations, iterated forecasts tend to outperform direct forecasts, particularly with longer lag model and with longer forecast horizons. Implementing SSVS prior generally improves forecasting performance over unrestricted VAR model for either nonstationary or stationary data.

The paper finds that iterated forecasts using model with the SSVS prior generally best outperform, suggesting that the SSVS restrictions on insignificant parameters alleviates over-parameterized problem of VAR in one-step ahead forecast and thus offers an appreciable improvement in forecast performance of iterated forecasts.

The novel coronavirus (SARS-CoV-2) variant of 2019 has taken more than 3.8 million lives according to the World Health Organization. To stop the spread of such a deadly and contagious disease, lockdown of varying nature was imposed worldwide. Lockdown, preventive techniques and observation of standard operating procedures (SOPs) have effectively decreased the spread of contagious diseases but have affected various businesses and industries economically. The food industry has been hit hard by different restriction parameters, due to which a disruption in food supply and demand was observed. Therefore, this study aims to study this disruption in the supply chain of processed food.

A comprehensive review was conducted on PubMed, Google Scholar, and Scopus to locate articles on processed foods, food delivery and supply chain. The selected articles were evaluated using the context analysis method.

The pandemic situation has increased the consumption and demand for processed food products from retail stores, and decreased the demand for food service products. These circumstances called for technological advancement in the field of food supply from farm to fork. This study reviews research articles, policies and secondary literature. Several advances have been made to deliver safe, nutritious and wholesome food to consumers. Block chain-based food supply chains, value stream mapping, sustainable supply chain domain and online ordering systems via mobile apps have been discussed in correspondence with information and communication technology (ICT) during COVID-19.

This study concludes that the use of advanced software and its adequate knowledge by suppliers, logistics companies and consumers have assisted in handling shocks to the global food system and provided in-time food delivery, traceability, database information and securely processed food to consumers.

This study shows the effects of COVID-19 pandemic on global food systems; disruption in food demand and supply chain is overlooked and changed; use of technological advances in food supply chain to tackle pandemic; online food ordering system gained popularity and improved technically.

1. The review highlights the effects of the COVID-19 pandemic on global food systems.

2. The disruption in food demand and supply chain is overlooked and changed.

3. The use of technological advances in the food supply chain to tackle the pandemic.

4. The online food ordering system gained popularity and improved technically.

The review highlights the effects of the COVID-19 pandemic on global food systems.

The disruption in food demand and supply chain is overlooked and changed.

The use of technological advances in the food supply chain to tackle the pandemic.

The online food ordering system gained popularity and improved technically.

The purpose of this study is to provide down-to-earth macroeconomic policy implications from the up-to-date estimates of the trade system in the OECD countries. Understanding on the linkages between the world trade mechanism and the macroeconomy is of utmost importance for the post-crisis managements of the world economy, the major points regarding the macroeconomic policy implications are as follows.

(1) For the majority of the OECD countries, fiscal expansion is likely to encourage the world trade when it is designed in the way to increase private consumption, in fact, only in a few countries fiscal expansion can increase the world trade volumes in its own right.

(2) Currency depreciation might be an attractive policy option for improving trade balances in the cases of the 9 OECD countries.

(3) There is a clear evidence of pricing-to-market with cross-country diversity, implying that import or domestic price robustness from the external forces.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

The Bass model was created to analyse the product life cycle (PLC) in order to help sales and marketing departments in their business decision making. The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments.

In this research, the authors analysed the 223,577 clients assisted by a nationwide network of car dealerships, who acquired 36,819 vehicles, during a 24-month period. In the analysis, the Bass model was applied to define the PLC phases; and nonlinear regression models were used to carry out the estimations.

The results show that more consistent estimates of the PLC phases are obtained from the clients assisted variable. This work has theoretical and practical implications that can help business management.

The most remarkable thing about this research is that we have shown that the functionality of the clients assisted variable is greater than the sales variable for the Bass model and, therefore, for PLC estimation.

The results of this research are very useful, since they allow marketing decision makers to obtain more consistent estimations of the PLC phases using the Bass model and the clients assisted variable. This is based on the fact that the use of this variable helps to detect if there is any deficiency in the design of the marketing strategy when the client does not make the purchase.

The data on clients assisted are as easily available to companies as sales data. However, the use of this variable improves PLC analysis and this allows an improvement in company forecasting. Thus, making the clients assisted variable a tool to strategically plan investments in innovation and marketing would reduce uncertainty in business management.

The purpose of this paper is to analyse the diferences between the clients assisted and sales variables, to discover which of the two variables is the more useful for the estimation of the PLC phases through the Bass model, thus aiding the managers of company sales and marketing departments.