Search results

Most existing studies on the impact of tourism on economic growth adopt an econometric approach that is insufficient to confirm that tourism actually leads to economic growth. Moreover, it cannot explain the causalities of different variables. Taking Mauritius as an example, this study aims to use the dynamic stochastic general equilibrium approach to investigate the contribution of tourism to economic growth when there is a productivity shock in the tourism sector.

A two-sector, small, open economy is modelled under the dynamic stochastic general equilibrium framework. The model is estimated using the Bayesian method based on real tourism and macroeconomic data from Mauritius for the period from 1999 to 2014. The impulse response functions are used to simulate the contribution of tourism to economic growth when there is a productivity shock in the tourism sector.

The simulation results show that the Mauritian gross domestic product (GDP) would increase by 0.09 per cent if the productivity of tourism is improved by 1 per cent, indicating that tourism could lead to economic growth. Considering the average annual growth rate of the Mauritian GDP, the contribution of tourism to its economic growth is significant. Furthermore, the effects of tourism on economic growth are moderated by price elasticities in international tourism demand.

This is the first study that estimates the dynamic stochastic general equilibrium model using the Bayesian method in tourism economic field. By correcting the prior information with real tourism and macroeconomic data, the estimation and simulation results are more robust compared with the calibration method, which has been used frequently in tourism studies.

The purpose of this paper is to introduce a 3R (right time, right place, and right material) principle for characterizing failure in humanitarian/relief supply chains’ response to natural disasters, and describes a Bayesian methodology of the failure odds with regard to external factors that may affect the disaster-relief outcome, and distinctive supply chain proneness to failure.

The suggested 3Rs combine simplicity and completeness, enclosing all aspects of the 7R principle popular within business logistics. A fixed effects logistic regression model is designed, with a Bayesian approach, to relate the supply chains’ odds for success in disaster-relief to potential environmental predictors, while accounting for distinctive supply chains’ proneness to failure.

Analysis of simulated data demonstrate the model’s ability to distinguish relief supply chains with regards to their disaster-relief failure odds, taking into account pertinent external factors and supply chain idiosyncrasies.

Due to the complex nature of natural disasters and the scarcity of subsequent data, the paper employs computer-simulated data to illustrate the implementation of the proposed methodology.

The 3R principle offers a simple and familiar basis for evaluating failure in relief supply chains’ response to natural disasters. Also, it brings the issues of customer orientation within humanitarian relief and supply operations to the fore, which had only been implicit within the humanitarian and relief supply chain literature.

The purpose of this paper is to investigate whether Markov mixture of normals (MMN) model is a viable approach to modeling financial returns.

This paper adopts the full Bayesian estimation approach based on the method of Gibbs sampling, and the latent state variables simulation algorithm developed by Chib.

Using data from the S&P 500 index, the paper first demonstrates that the MMN model is able to capture the unconditional features of the S&P 500 daily returns. It further conducts formal model comparisons to examine the performance of the Markov mixture structures relative to two well‐known alternatives, the GARCH and the t‐GARCH models. The results clearly indicate that MMN models are viable alternatives to modeling financial returns.

The univariate MMN structure in this paper can be generalized to a multivariate setting, which can provide a flexible yet practical approach to modeling multiple time series of assets returns.

Given the encouraging empirical performance of the MMN models, it is hopeful that the MMN models will have success in some interesting financial applications such as Value‐at‐Risk and option pricing.

The paper explicitly formulates the Gibbs sampling procedures for estimating MMN models in a Bayesian framework. It also shows empirically that MMN models are able to capture the stylized features of financial returns. The MMN models and their estimation method in this paper can be applied to other financial data, especially in which tail probability is of major interest or concern.

The purpose of this paper is to consider the stochastic differential equation of the RL electrical circuit as the dynamic model of a state space system when the current in the circuit is hidden and corrupted by the measurement noise. Estimation of the corrupted current and the values of missing or unknown parameters (resistance, the observed current variance in the measurement model, the mean and variance of the current prior distribution) which are the main concern in electrical engineering is considered.

Optimal filtering is proposed for estimation of the hidden current from the noisy observations. Also, the problem of analyzing this model based on estimation of the unknown parameters is addressed from the likelihood‐based and Bayesian perspective.

Computational techniques for parameter estimation are carried out by the Maximum likelihood (ML) approach using Expectation‐Maximization type optimization and Bayesian Monte Carlo perspective using Metropolis‐Hastings scheme. The explicit formulas for the ML estimator are obtained and it is shown that the smoothers, the filters and the predictions for the current have the best confidence intervals, respectively. Some numerical simulation examples which are performed by R programming software are considered to show the efficiency and applicability of the proposed approaches. Results show an excellent estimation of the parameters based on these approaches.

Due to the fact that in an empirical situation of electrical engineering, observing the current in the circuit regardless of the measurement noise and knowing the exact value of the parameters are unrealistic assumptions, this paper can be used in various types of real time projects.

To the best of the authors' information, the problem of analyzing the state space model of RL electrical circuit has not been studied before. Furthermore, the estimation of the hidden current as the state of the system and estimation of the unknown parameters of the model via both ML and Bayesian approaches have been investigated for the first time in the present study.

The paper aims to examine the impact of social capital on the size of the shadow economy in the BIRCS countries over the period 1995–2014.

The authors employ the Bayesian linear regression method to uncover the relationship between social capital and the shadow economy. The method applies a normal distribution for the prior probability distribution while the posterior distribution is determined using the Markov chain Monte Carlo technique.

The results indicate that the unemployment rate and tax burden positively affect the size of the shadow economy. By contrast, corruption control and trade openness are negatively associated with the development of this informal sector. Moreover, the paper's primary finding is that social capital represented by social trust and tax morale can hinder the size of the shadow economy.

This study is limited to the case of the BRICS countries for the period 1995–2014. The determinants of the shadow economy in different groups of countries can be heterogeneous. Moreover, social capital is a multidimensional concept that may consist of various components. This difficulty of measuring the social capital calls for further research on the relationship between other dimensions of social capital and the shadow economy.

Many studies investigate the effect of economic factors on the size of the shadow economy. This paper applies a new approach to discover the issue. Notably, the authors use the Bayesian linear regression method to analyze the relationship between social capital and the shadow economy in the BRICS countries.

This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.

The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian stochastic method is developed including model selection and estimation of the tree structure parameters. The framework is applied on numerous U.S. asset pricing models, using alternative mimicking factor portfolios, frequency of data, market indices, and equity portfolios.

The findings reveal strong evidence that asset returns exhibit asymmetric effects and non- linear patterns to different common factors, but, more importantly, that there are multiple thresholds that create several partitions in the common factor space.

To the best of the authors' knowledge, this paper is the first to explore and apply a tree-structured and quantile regression framework in an asset pricing context.

The purpose of this paper is to examine the ability of hedge funds and funds of hedge funds to generate absolute returns using fund level data.

The absolute return profiles are identified using properties of the empirical distributions of fund returns. The authors use both Bayesian multinomial probit and frequentist multinomial logit regressions to examine the relationship between the return profiles and fund characteristics.

Some evidence is found that only some hedge funds strategies, but not all of them, demonstrate higher tendency to produce absolute returns. Also identified are some investment provisions and fund characteristics that can influence the chance of generating absolute returns. Finally, no evidence was found for performance persistence in terms of absolute returns for hedge funds but some limited evidence for funds of funds.

This paper is the first attempt to examine the hedge fund return profiles based on the notion of absolute return in great details. Investors and managers of funds of funds can utilize the identification method in this paper to evaluate the performance of their interested hedge funds from a new angle.

Using the properties of the empirical distribution of the hedge fund returns to classify them into different absolute return profiles is the unique contribution of this paper. The application of the multinomial probit and multinomial logit models in the fund performance and fund characteristics literature is also new since the dependent variable in the authors' regressions is multinomial.

The purpose of this paper is to propose a game-theoretical model for commercial bank foreign direct investment strategy, government policy and domestic banking industry interactions in emerging market economies and demonstrate the application of this strategy to the banking system. Government policy and domestic banking industry interactions in emerging market economies and demonstrate the application of this strategy to the banking system.

The paper develops a game-theoretical model to analyze the optimality of the limiting entry strategy followed by a given domestic institutional sector when considering the entry applications of foreign banks in the domestic financial system. The model analyzes the strategic options available to an emerging market country with a relatively underdeveloped banking system when deciding whether or not and to what extent allow for the entrance of better reputed and more technologically advanced foreign banks in its domestic financial system.

The paper shows that the progressive liberalization of entry restrictions would define the perfect Bayesian equilibria of the subsequent set of continuation games and the respective payoffs derived from this liberalization as the domestic economy integrates and competes within the global financial system.

Banks operating in the international financial market have incentives to invest directly in emerging market economies and governments have incentives in allowing foreign banks entry to their market. As banking systems in these economies are generally underdeveloped, opening the financial system to foreign competitors could lead to a decrease in the market share of local banks. Eventually foreign banks could control the banking system and could de facto control the money supply.

Despite the practice of credit card services by Islamic financial institutions (IFIs) is debatable, Islamic banks (IBs) have been offering this product. Both Muslim and non-Muslim customers have subscribed to the products. Thus, it is critical to analyse the strategy of IBs’ moral messages in reminding their Muslim and non-Muslim customers to repay their credit card debts. This paper aims to investigate this issue in Indonesia using data mining via machine learning.

This study examines the IBs’ customers across the 32 provinces of Indonesia regarding their moral status in credit card debt repayment. This work considers 6,979 observations of the variables that affect the moral status of the IBs’ customers in repaying their debt. The five types of data mining via machine learning (i.e. Boruta, logistic regression, Bayesian regression, random forest, XGBoost and spatial cluster) are used. Boruta, random forest and XGBoost are used to select the important features to investigate the moral aspects. Bayesian regression is used to get the odds and opportunity for the transition of each variable and spatially formed based on the information from the logistical intercepts. The best method is selected based on the highest accuracy value to deliver the information on the relationship between moral status categories in the selected 32 provinces in Indonesia.

A different variable on moral status in each province is found. The XGBoost finds an accuracy value of 93.42%, which the three provincial groups have the same information based on the importance of the variables. The strategy of IBs’ moral messages by sending the verse of al-Qur’an and al-Hadith (traditions or sayings of the Prophet Muhammad PBUH) and simple messages reminders do not impact the customers’ repaying their debts. Both Muslim and non-Muslim groups are primarily found in the non-moral group.

This study does not consider socio-economic demographics and culture. This limitation calls future works to consider such factors when conducting a similar topic.

The industry professionals can take benefit from this study to understand the Indonesian customers’ moral status in repaying credit card debt. In addition, future works may advance the recent findings by considering socio-cultural factors to investigate the moral status approach to Islamic credit warnings that is not covered by this study.

This work finds that religious text of credit card repayment reminders sent to Muslims in several provinces of Indonesia does not affect their decision to repay their debts. To some extent, this finding draws a social issue that the local IBs need to consider when implementing the strategy of credit card repayment reminders.

This study credits a novelty in the discourse of data science for Islamic finance practices. Specifically, this study pioneers an example of using data mining to investigate Islamic-moral incentives in credit card debt repayment.

The purpose of this paper is to draw on self‐categorisation theory and nation branding to investigate the social identities and influences which underpin consumer preferences for national brands.

A survey in Mozambique, an underdeveloped African country, compared a domestic mobile phone company whose brand contains the country name against a European brand. Consumer ethnocentrism might arise identifying with the national brand or with Mozambican personalities endorsing the brand. Value‐expressiveness might arise from consumers associating with celebrity endorsers. A dichotomy of youth versus older consumers moderated the relationships between social identities and brand preference. Bayesian structural equation modelling using Monte Carlo simulations estimated the path coefficients from a sample of 611.

Across age groups, ethnocentrism is stronger than value‐expressiveness in determining preference for national brands. Moreover, ethnocentrism is stronger with the older rather than younger consumers. Consumer ethnocentrism stemmed mainly from injunctive influence (IN) with both age groups. With older consumers, value‐expressiveness related significantly to descriptive influence, but not to IN. With youth, neither social influence significantly related to value‐expressiveness.

Single‐item measures might be less effective than multi‐item measures for psychological concepts of social identities and influences.

Understanding the role of social identity in consumer preferences for national brands may help managers heighten consumers' social identities and increase their loyalty for national brands. Shedding light on under‐researched African consumers may help firms doing business in these emerging markets as well as African governments that are attempting to strengthen the perceptions of their nation brand.

This paper bridges research in social psychology and international marketing by investigating the social identities and influences that underpin consumer preferences for national brands.