Search results

Indian railways (IR) is one of the largest railway networks in the world. As a part of its strategic development initiative, demand forecasting can be one of the indispensable activities, as it may provide basic inputs for planning and control of various activities such as coach production, planning new trains, coach augmentation and quota redistribution. The purpose of this study is to suggest an approach to demand forecasting for IR management.

A case study is carried out, wherein several models i.e. automated autoregressive integrated moving average (auto-ARIMA), trigonometric regressors (TBATS), Holt–Winters additive model, Holt–Winters multiplicative model, simple exponential smoothing and simple moving average methods have been tested. As per requirements of IR management, the adopted research methodology is predominantly discursive, and the passenger reservation patterns over a five-year period covering a most representative train service for the past five years have been employed. The relative error matrix and the Akaike information criterion have been used to compare the performance of various models. The Diebold–Mariano test was conducted to examine the accuracy of models.

The coach production strategy has been proposed on the most suitable auto-ARIMA model. Around 6,000 railway coaches per year have been produced in the past 3 years by IR. As per the coach production plan for the year 2023–2024, a tentative 6551 coaches of various types have been planned for production. The insights gained from this paper may facilitate need-based coach manufacturing and optimum utilization of the inventory.

This study contributes to the literature on rail ticket demand forecasting and adds value to the process of rolling stock management. The proposed model can be a comprehensive decision-making tool to plan for new train services and assess the rolling stock production requirement on any railway system. The analysis may help in making demand predictions for the busy season, and the management can make important decisions about the pricing of services.

With the rapid low-carbon transformation in China, the industrial approach and labor structure of mining enterprises are undergoing constant changes, leading to an increasing psychological dilemma faced by coal miners. This study aims to reveal the relationship and mechanism of factors influencing the psychological dilemma of miners, and to provide optimal intervention strategies for the safety and sustainable development of employees and enterprises.

To effectively address the complex issue of the psychological dilemma faced by miners, this study identifies and constructs five-dimensional elements, comprising 20 indicators, that influence psychological dilemmas. The relational mechanism of action of factors influencing psychological dilemma was then elucidated using an integration of interpretive structural modeling and cross-impact matrix multiplication.

Industry dilemma perception is a “direct” factor with dependent attributes. The perceptions of management response and relationship dilemmas are “root” factors with driving attributes. Change adaptation dilemma perception is a “susceptibility” factor with linkage attributes. Work dilemma perception is a “blunt” factor with both dependent and autonomous attributes.

The aforementioned findings offer a critical theoretical and practical foundation for developing systematic and cascading intervention strategies to address the psychological dilemma mining enterprises face, which contributes to advancing a high-quality coal industry and efficient energy development.

The paper investigates the dynamic relationship among the stock markets of South Asian Association of Regional Cooperation (SAARC) countries during the COVID-19 pandemic.

Daily time-series data of four SAARC countries: India, Bangladesh, Pakistan, and Sri Lanka, from February 13th, 2013 to March 31st, 2021 are used. The study considers stock prices prior to the blowout of COVID-19 and during the onset of the pandemic. The novel estimation procedure of the autoregressive distributed lag model is used while the results are also confirmed by post-estimation techniques.

The study confirms that the COVID-19 contagion has adversely influenced the stock returns of SAARC countries. The findings signify that the pattern of cointegration has significantly different regularities in the pattern of causality in the long run and short run during the COVID-19 crisis. Overall, the study revealed that the COVID-19 pandemic has weakened the dynamic connection among the stock markets of SAARC countries.

To dampen uncertainties generated by the COVID-19 pandemic, the authorities and central banks should be equipped with efficient strategies and guidelines to cope with the crisis created by the pandemic. Further, governments should focus on assuaging the panic faced by investors and enhancing the confidence of domestic as well as foreign investors. Further, the weakened integration of financial markets during the crisis offers opportunities for speculative and arbitrage gains for investors.

The research work is an innovative effort to analyze the impression led by COVID-19 on the SAARC stock markets integration.

This paper aims to examine the impact of COVID-19 on inflation in Indonesia. There are two questions in this study: (1) Is there an impact of COVID-19 on inflation in Indonesia? and (2) whether there are differences in the impact of COVID-19 on regional inflation in Indonesia, considering the different intensities associated with COVID-19.

The estimation technique showing the impact of the COVID-19 pandemic on inflation uses the difference-in-differences (DID) method described by Pischke (2008). The core idea of the estimation above is continuous DID using panel data. No province was affected by COVID-19 before 2020:Q1. Once COVID-19 hits the economy, the effects vary from one district to the other.

The authors find that the severity of the COVID-19 pandemic negatively affects inflation – the more severe the pandemic, the lower the inflation. This finding conforms with several studies suggesting higher demand pressures than supply during the pandemic. Compared with supply-side indicators such as production index, demand-side indicators – such as consumer confidence index and real sales index – fell more sharply.

In the Introduction section, the authors have added a discussion that indeed the COVID-19 pandemic affects inflation through both the demand- and supply-side shocks. While factors driving regional differences in inflation rate are important research and policy questions, the analysis of these factors is outside the scope of this study. The study focuses on the COVID-19 impact on inflation and whether the pandemic disproportionately affects some regions than the others.

This research is important to provide an understanding of the nature of the pandemic on inflation in the context of the Indonesian economy, which is essential to policy formulation, especially for the Central Bank in carrying out the mandate to maintain rupiah stability. This issue is due to the implications of different policy responses between demand- and supply-side shocks.

As a novelty in this study and research gap, the authors use a continuous DID method to account for the varying intensity of COVID-19 across the provinces. In particular, the authors use the number of positive cases of COVID-19 per 1,000 population as opposed to just a binary indicator of before-and-during COVID-19 across provinces.

The purpose of this study is to analyze how sentiment affects economic activity in Brazil.

Based on a nonlinear autoregressive distributed lag (NARDL) model, this study examines in detail the short-term and long-term asymmetric impacts between the variables during the period from January 2007 to December 2020.

There are three main results of this study. First, sentiment is an important factor for economic activity in Brazil, and its effect possibly occurs through the channels of consumption and investment, which are the two main components of economic growth. Second, sentiment affects economic activity in different ways in the short and the long term: in Brazil, although in the short-term, immediate shocks of sentiment may be confusing, the negative shocks from previous periods have a negative impact on economic activity. Third, the effect of shocks of optimism and pessimism on economic activity is asymmetric, and in the long run, only shocks of optimism have a significant and positive impact.

The relationship between sentiment and economic activity is still a controversial issue in the literature and this study seeks to advance its understanding in Brazil.

As one of the world's most valuable traded commodities, the market for coffee beans has grown enormously in recent years. The paper aims on analyzing the nonlinear exchange rate pass-through in Turkish coffee bean imports from two important sources in South America: Brazil and Colombia.

Data collected in this paper through reliable channels include nominal import value, exchange rate, production of total industry, etc. Independent and dependent variables are obtained through conversion. Since the nonlinearly adjusted exchange rate differs significantly from the linearly adjusted one for the export trade of Brazilian coffee beans, this paper develops the autoregressive distributed lag (ARDL) and nonlinear ARDL frameworks and demonstrates their application through asymmetric cointegration and error correction models.

The results of this paper show that imports of Brazilian coffee bean exhibit a more dramatic asymmetry compared to Colombia's coffee bean imports. The results of this study contribute to the import trade of non-oil commodities in developing countries, particularly Brazil, and enrich the existing literature on nonlinear exchange rate adjustments.

The export of Colombian coffee beans is not as old as Brazil, and it was not until much later that Colombia began to export coffee beans to the rest of the world.

The present study is an addition to the literature of agricultural trade. The authors analyze the nonlinear exchange rate pass-through in Turkish coffee bean imports from two important sources in South America: Brazil and Colombia. Different from the current mainstream research on oil commodity trade, this paper focuses on international trade from the perspective of coffee beans, which can enlighten the practice in this field.

The present study, by using signaling perspective aims to investigate short-term valuation impact of rebranding announcements (with name change) on stock performance of 160 service firms listed on NSE NIFTY-500 over the period of 2000–2019.

An event study methodology is used to estimate the cumulative abnormal returns (CARs) and its statistical significance is tested with both parametric and non-parametric test-statistics. Separate analysis has been conducted for firms with “major vs minor” and “restructuring vs non-restructuring” name change.

Findings of the study suggest that rebranding decisions are negatively associated with abnormal returns around the announcement period indicating strong disapproval of name change event. In addition, investors formed strong adverse opinion for major name change firms as compared to minor name change firms. Further, restructured name change sample document larger negative drift than non-restructured sample.

Findings offer substantial repercussions for shareholders who can make informed judgments about name change as a signal of reinventing brand identity. Managers should announce detailed rationale behind name change decision to market for enhancing corporate reputation.

This study contributes to marketing-finance interface literature and is first to examine market reaction to name change of Indian service firms and moreover, made a distinction between major vs minor and restructured vs non-restructured name change events for these firms.

The purpose of this study is to determine whether the Ottoman Empire’s net interest payments and foreign debt were sustainable or not in terms of their burden on budget revenues, using the method of historical econometric analysis.

In this study, the period between 1847 and 1882 of the Ottoman Empire is analyzed for sustainability analysis. Within the framework of the study, unit root tests and econometric analysis methods frequently used in the literature were used to analyze the sustainability of public debt. In the econometric analysis, in addition to various unit root tests, current econometric analysis methods, in particular Fourier expansion, were also used.

The results of econometric analyses showed that the burden of interest payments and foreign debt on the budget of the Ottoman state was unsustainable. This situation clearly shows the reason for the official bankruptcy of the Ottoman Empire, which was declared in 1875.

Although this study reveals the bankruptcy process of an important structure such as the Ottoman Empire in the historical process through econometric analyses, it also gives a very important message to today’s states. Accordingly, today’s state policies and decision-making mechanisms should take these results into account and strive to make the burden of public interest payments sustainable. It is believed that the study will shed light on the public finance policies of today’s states by drawing lessons from the collapse process of the Ottoman state.

Unlike the historical assessments in the literature on the decline of the Ottoman Empire, this study presents a cliometric approach by applying current econometric analysis techniques to past historical data. The study explains the unsustainability of the Ottoman Empire’s interest payments and external debt burden in the period under consideration in a way that, to the best of the authors’ knowledge, has not been done before.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

In the context of the digital age, this study aims to investigate the impact of citizens' digital participation on the scientific and democratic decision-making processes of the government. Specifically, the authors focus on the factors influencing citizens' digital participation, with a particular emphasis on their digital skills.

Exploring the influence of citizens' digital skills on their digital participation is of great practical significance for eliminating the digital divide and for promoting a life characterized by enriched digital interactions with the public. This study selected the social consciousness survey database of Chinese netizens in 2017, used ordered Probit and OLS models, and comprehensively used the instrumental variable method (IV), causal stepwise regression method and bootstrap method to empirically verify and construct a mechanism model of the influence of digital skills on citizens' digital participation.

The empirical findings indicate a noteworthy positive association between citizens' proficiency in digital skills and their active engagement in digital activities. This relationship is positively mediated by factors such as political interest and attention to social issues, underscoring their role in encouraging greater digital participation. Conversely, national identity exhibits a counteractive influence on this mechanism, potentially discouraging digital engagement. Notably, the impact of digital skill mastery on digital participation is more pronounced among non-elderly individuals and those residing in metropolitan areas, highlighting the significance of demographic characteristics in this context.

These research results can help the government and other organizations make better decisions and facilitate improvement of citizens' digital participation by promoting their mastery of digital skills.