Search results

This study aims to analyze Kickstarter data along with social media data from a data mining perspective. Kickstarter is a crowdfunding financing plataform and is a form of fundraising and is increasingly being adopted as a source for achieving the viability of projects. Despite its importance and adoption growth, the success rate of crowdfunding campaigns was 47% in 2017, and it has decreased over the years. A way of increasing the chances of success of campaigns would be to predict, by using machine learning techniques, if a campaign would be successful. By applying classification models, it is possible to estimate if whether or not a campaign will achieve success, and by applying regression models, the authors can forecast the amount of money to be funded.

The authors propose a solution in two phases, namely, launching and campaigning. As a result, models better suited for each point in time of a campaign life cycle.

The authors produced a static predictor capable of classifying the campaigns with an accuracy of 71%. The regression method for phase one achieved a 6.45 of root mean squared error. The dynamic classifier was able to achieve 85% of accuracy before 10% of campaign duration, the equivalent of 3 days, given a campaign with 30 days of length. At this same period time, it was able to achieve a forecasting performance of 2.5 of root mean squared error.

The authors carry out this research presenting the results with a set of real data from a crowdfunding platform. The results are discussed according to the existing literature. This provides a comprehensive review, detailing important research instructions for advancing this field of literature.

The purpose of this paper is to examine the relationship between urbanization and economic growth in ASEAN countries for the period 1993-2014.

The Granger causality test and the regression estimation method with static and dynamic panel data (FE, RE, Driscoll and Kraay, D-GMM and PMG) were used. The sample includes seven ASEAN countries: Brunei, Cambodia, Indonesia, Malaysia, Philippines, Thailand and Vietnam.

The results show that at least a causal relationship exists between urbanization and economic growth and urbanization positively impacts economic growth. However, the relationship between urbanization and economic growth is non-linear. The urbanization reaches a threshold after which it may impede the economic growth. The estimated threshold is 69.99 percent for the static model and 67.94 percent for the dynamic model.

The evidence from this study suggests that there is a non-linear relationship between urbanization and the economic growth. Urbanization has the potential to accelerate the economic growth, and this potential will depend on the establishment of favorable institutions and investments in appropriate public infrastructure.

The decision on the model of urbanization needs to be based on social and environmental considerations as well as market-based economic efficiency. The quality of urbanization manifests in the way that people and businesses perceive when they come to cities and their position in the labor market, urban housing, niche commodity markets, supply chain, collaborative network and physical space for the operation of the business. Most ASEAN countries have not yet reached a high level of urbanization, despite having a number of policies for promoting urbanization to contribute to the economic growth. However, policymakers should find ways to facilitate the development of urbanization that contributes to economic growth, employment growth, environmental sustainability, rather than the pursuit of speeding up the process of urbanization.

Between urbanization and economic growth at least a causal relationship exists. Urbanization positively impacts economic growth. However, the relationship between urbanization and economic growth is non-linear. The urbanization reaches a threshold after which it may impede the economic growth. The estimated threshold is 69.99 percent for the static model and 67.94 percent for the dynamic model.

This study aims to explore the relationship between the growth threshold effect on renewable energy consumption (REC) in the major oil-producing countries in sub-Saharan Africa (SSA) over the period 1990–2018.

This article used a dynamic panel threshold regression model introduced by Hansen (1996, 1999 and 2000) threshold (TR) models. The procedure is achieved using 5,000 bootstrapping replications and the grid search to obtain the asymptotic distribution and p-values. For the long-run relationship among our variables, the author followed the process in Pesaran et al. (1999) pooled mean group (PMG) for heterogeneous panels. Furthermore, for the robustness of our empirical results due to the sensitivity of the results to outliers, the author used the approach by Cook (1979) distance measure. The author applied quantile (QR) regression to explore the distribution of dependent variables following Bassett and Koenker (1982) and Koenker and Bassett (1978) approaches.

The results from the threshold effect test and threshold regression revealed a significant single threshold effect of growth level on REC. Furthermore, the result from the PMG estimation showed the growth of the variable, energy intensity, consumer prices and CO₂ emissions play a significant role in REC in major oil-producing countries in SSA. The growth threshold estimation results indicated one significant threshold value of 1.013% at one period lagged of real growth. The outlier’s sensitivity detention greatly influenced our empirical results.

The article filled the literature gap by applying a combined measure that is robustness to detect outliers in the data, which none of the studies in the literature addresses hitherto. Further, the article extends the quantile regression to growth – REC literature.

This research investigates the threshold effects of the interplay between finance, development and carbon emissions across 97 countries, including 50 low-income and 47 high-income countries, during the period from 1991 to 2019.

Employing various econometric modeling techniques such as dynamic linear regression, dynamic panel threshold regression and in/out of sample splitting, this study analyzes the data obtained from the World Bank's world development indicators.

The results indicate that low-income countries require a minimum financial development threshold of 0.354 to effectively reduce carbon emissions. Conversely, high-income countries require a higher financial development threshold of 0.662 to mitigate finance-induced carbon emissions. These findings validate the presence of a finance-led Environmental Kuznet Curve (EKC). Furthermore, the study highlights those high-income countries exhibit greater environmental concern compared to their low-income counterparts. Additionally, a minimum GDP per capita of US$ 10,067 is necessary to facilitate economic development and subsequently reduce carbon emissions. Once GDP per capita surpasses this threshold, a rise in economic development by a certain percentage could lead to a 0.96% reduction in carbon emissions across all income levels.

This study provides a novel contribution by estimating practical financial and economic thresholds essential for reducing carbon emissions within countries at varying levels of development.

This study aims to analyze the factors that influenced the stability of Islamic banks in Asia.

The panel data consisted of 16 Asian countries operating Islamic banks from 2010 to 2019. The data were analyzed through dynamic panel regression using Arellano–Bond generalized method of moments (GMM).

This study provides novel insights into the factors influencing the stability of Islamic banks in Asia. The findings suggest that past financial stability, liquidity risk, loan risk, inflation, gross domestic product, government effectiveness, rule of law and control of corruption are all significant contributors to Islamic bank stability. Notably, political stability, voice and accountability and regulatory quality did not show a significant association.

The current study’s focus was solely on Islamic bank stability in Asian countries, which leaves room for further exploration. Future research could benefit from expanding the scope to encompass all nations with active Islamic banking institutions. In addition, incorporating a broader range of macroeconomic variables, such as exchange rates, interest rates, profit-sharing equivalents and investment rates, could provide deeper insights into the factors influencing Islamic bank stability across diverse contexts.

This study has significant practical implications for policymakers, bank managers and regulatory authorities seeking to enhance the stability of Islamic banks in Asia. By implementing robust risk management frameworks, adopting prudent regulatory policies, and actively fostering economic growth, policymakers can create an environment conducive to the sustained development and prosperity of Islamic banking institutions. Notably, promoting good governance practices and instituting effective crisis prevention measures can further bolster the resilience of the Islamic banking sector, enabling it to play a more dynamic role in contributing to the overall development and welfare of Asian societies.

The findings of this study carry significant social implications, highlighting the need for governments in Asian countries to prioritize public policies that promote good governance and ethical practices within the banking industry. Such policies, coupled with efforts to attract foreign investments and foster a stable and transparent banking sector, have the potential to generate far-reaching positive effects on society. Through economic growth stimulated by a robust Islamic banking sector, Asian countries can create new employment opportunities, improve living standards and ultimately enhance the overall well-being of their citizens.

This study contributes to the ongoing discourse on Islamic banking stability by offering novel insights and expanding the empirical knowledge base in this field. The dual application of robust regression methodologies – namely, GMM dynamic panel data models – presents a unique analytical framework for investigating the complex interplay between diverse variables and Islamic bank stability. This methodological choice fosters deeper understanding of the dynamic relationships at play, advancing our understanding of how specific factors influence the sector's resilience and performance. In addition, the study uses rigorous empirical techniques and engages with the extant literature to provide fresh perspectives and nuanced interpretations of the findings, further solidifying its contribution to the field's originality and richness.

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selection (DMS) or averaging (DMA) in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact method for implementing DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an inflation forecasting application. We find strong evidence of model switching. We also compare different ways of implementing DMA/DMS and find forgetting factor approaches and approaches based on the switching Gaussian state space model to lead to similar results.

In recent years, the Indian banking sector is facing a major cause of concern in the form of Nonperforming Assets (NPA), and the priority sector lending (PSL) is generally recognized as the major factor contributing to it. Thus, the present study has been carried out with the objective of examining the relationship between priority sector lending and GDP growth. Thereafter, the role of PSL and certain other bank-specific, industry-specific and macroeconomic variables in determining NPA has been studied.

Taking a sample of 45 scheduled commercial banks, the study has been carried out for 14 years (2004–2018). Granger causality between PSL and GDP has been examined by applying the Dumitrescu-Hurlin test. For the purpose of investigating the impact of PSL and other determinants on NPA, both static and dynamic panel regression have been performed. Under the dynamic panel, system generalized methods of moments (S-GMM) approach has been followed.

The findings show that there exists a positive correlation and bidirectional causal relationship between PSL and GDP, which implies that PSL brings additional growth for the whole economy. In addition to it, PSL is found to be insignificant for the NPA ratio, and thus, it can be inferred that credit extended to government-specified sectors does not bring any major increase in the bad loan portfolio of banks.

The policymakers and bank management can take a cue from the findings of this study to decrease the exposure to loan nonrepayment issue. The priority sectors are in need of formal credit for their growth, and since the rising population of the country can find employment in these sectors, banks should meet their credit needs while securing their position with regard to the NPA problem.

The issue of NPA determinants, and in particular, the contribution of priority sector lending in it has not been much explored for Indian banking sector. Also, the present study adds to the literature by using the causality approach for examining the importance of directed credit schemes for economic growth.

This study aims to investigate a new determinant of corporate cash holdings of Australian energy firms: economic policy uncertainty (EPU). Based on two motives for holding cash: precautionary and speculative motives, the authors argue that EPU increases financing constraints or induces firms to postpone investment projects, thereby increasing their cash holdings. The authors examine whether the Australian policy-related economic uncertainty affects cash holdings of Australian energy companies.

This research uses a data set of Australian energy firms from 2010 to 2020 and the Australian EPU index, which measures the uncertainty in economic policy, using news coverage of eight major Australian newspapers. To address the potential endogeneity bias and ensure the robustness of the results, three models are used: ordinary least squares, fixed-effects and dynamic generalized method of moments.

The authors find that the EPU index has a significant and positive effect on cash holdings, after controlling for firm-specific factors. While firm size and dividend payments have mixed and insignificant effects, other determinants are significant, such as growth opportunities, net working capital, cash flow, cash flow risk, leverage and capital expenditure. The authors also find that the positive effect of EPU on cash holdings is not the manifestation of EPU affecting corporate investments but rather explained by financing constraints.

The findings have implications for policymakers and regulators in Australia as the uncertainty of their economic policies plays an important role when Australian energy companies determine their cash holding level to manage liquidity risks.

This study is the first to document EPU index as the new determinant of corporate cash holdings of Australian energy companies. Firms in this sector have a great need of funding and liquidity for their operations and capital-intensive projects. High EPU index induces them to hold more cash to avoid liquidity shocks.

This study aims to link environmental disclosure quality (EDQ) to firm performance and examine the moderating role of board independence in this relationship.

Drawing on agency theory and stakeholder theory, the authors developed and tested hypotheses using original survey data from 720 firm-year observations collected from 120 Iranian companies over six years between 2011 and 2016. In this paper, they conducted static and dynamic panel data analysis.

After correcting for endogeneity bias, the results showed that there is a significant positive relationship between EDQ and firm performance. The results also showed that board independence significantly reinforces the positive effect of EDQ on performance, and firms with more independent board members are involved environmental disclosure for improved performance. This is consistent with agency theory, which posits that a more independent board of directors can better monitor the CEO and reduce incentives for pursuing personal interests, which in turn improves performance. The results are robust after performing sensitivity tests.

This paper takes the perspective of corporate governance to empirically examine the effect of EDQ on firm performance. This study makes a contribution to the literature by showing that board independence moderates the effects of EDQ on firm performance.

The evidence supports the emphasis that recent policy statements have put on increasing the number of independent directors on corporate boards. This study offers insights to policymakers interested in enhancing the monitoring role of corporate boards.

The study adds value to the understanding of the effect of the EDQ on performance and how board independence influences this relationship, particularly in an emerging economy like Iran.

The aim of this article is to research on forestry green total factor productivity and explore the impact of financial support on forestry green total factor productivity.

The methods used in this study are super efficiency SBM model of undesired output and empirical model. SBM model is a kind of Data Envelopment Analysis (DEA). The SBM model with non-expected outputs (slacks-based measure) can be used to deal with the problem of efficiency measurement with multiple input and output variables and can be used to analyze the efficiency of green development of forestry economy.

First, the overall green total factor productivity of the authors’ country's forestry has shown a trend of first decline and then an increase from 2008 to 2018, and there are significant spatiotemporal differences; second, financial support has a significant positive impact on forestry green total factor productivity; third, environmental regulation has a significant threshold effect in the process of financial support on forestry green total factor productivity, and the role of financial support shows a trend of first increasing and then decreasing.

Secondly, taking the data of 30 provinces and cities in the authors’ country from 2008 to 2018 as the research object, using the super-efficiency SBM-Malmquist index to measure the country's forestry green total factor productivity and analyze its temporal and spatial changes; finally, a dynamic panel model was established to explore the impact of financial support on forestry green total factors quantitative impact on productivity, and adding environmental regulation as a threshold variable to establish a dynamic threshold regression, and found that financial support has a nonlinear impact on forestry green total factor productivity.