Method Of Moments Research Articles

IMPLEMENTASI METODE TREND MOMENT UNTUK PREDIKSI PENJUALAN (STUDI KASUS DI TOKO ZACOZI PANCING KABUPATEN SOLOK)

Zacozi Fishing Shop is a shop that sells various kinds of fishing equipment, from small to large. Through direct research at the Zacozi Pancing Shop, the problem was found that from year to year sales of fishing equipment at this shop experienced an erratic increase and decrease in sales every month. This happens because many goods are not sold out and there are many goods that consumers need but the stock of goods requested by consumers is insufficient. This is also because the shop does not have a system that can predict sales, so many products are not available for a long time. Therefore, it is necessary to have a sales prediction information system using the Trend Moment method to be able to predict sales of goods in the following month. The data used is fishing rod sales data from June 2022 to June 2023. With a sales prediction system using the Trend Moment method which can produce sales predictions for goods at the Zacozi Pancing Shop with an average success rate of 90%, it can help the shop in picking Actions that should be taken, especially in controlling product availability using the Trend Moment method so that stock shortages and excesses can be avoided.

On the atypical solutions of the symmetric binary perceptron

We study the random binary symmetric perceptron problem, focusing on the behavior of rare high-margin solutions. While most solutions are isolated, we demonstrate that these rare solutions are part of clusters of extensive entropy, heuristically corresponding to non-trivial fixed points of an approximate message-passing algorithm. We enumerate these clusters via a local entropy, defined as a Franz–Parisi potential, which we rigorously evaluate using the first and second moment methods in the limit of a small constraint density α (corresponding to vanishing margin κ ) under a certain assumption on the concentration of the entropy. This examination unveils several intriguing phenomena: (i) we demonstrate that these clusters have an entropic barrier in the sense that the entropy as a function of the distance from the reference high-margin solution is non-monotone when κ⩽1.429−α/log⁡α , while it is monotone otherwise, and that they have an energetic barrier in the sense that there are no solutions at an intermediate distance from the reference solution when κ⩽1.239−α/log⁡α . The critical scaling of the margin κ in −α/log⁡α corresponds to the one obtained from the earlier work of Gamarnik et al (2022 (arXiv:2203.15667)) for the overlap-gap property, a phenomenon known to present a barrier to certain efficient algorithms. (ii) We establish using the replica method that the complexity (the logarithm of the number of clusters of such solutions) versus entropy (the logarithm of the number of solutions in the clusters) curves are partly non-concave and correspond to very large values of the Parisi parameter, with the equilibrium being reached when the Parisi parameter diverges.

Comprehensive Board of Commissioner Diversity and Corporate Social Responsibility Disclosure in Indonesia

This study examines the effect of comprehensive board of commissioner diversity (BOC_DIV) and corporate responsibility (CSR) disclosure for listed companies in Indonesia from 2019 – 2020. The implementation of CSR is growing in emerging markets, including Indonesia. In the context of the dual board system in Indonesia, the role of the board of commissioner is vital to address the growing CSR issue through their monitoring and supervising functions. We based our study on Indonesian-listed firms across industries. However, we exclude the financial sector due to different policies. Multiple Regression is employed to investigate the relationship, and the Generalized Method of Moment (GMM) model is executed to identify whether our model falls into the endogeneity problem. The results showed that several Board of Commissioner characteristics (such as size, independence, and gender) are positively associated with CSR disclosure. After testing for the endogeneity problem, the findings remain similar. We examine several dimensions of the board of commissioner diversity on non-financial outcomes, especially CSR disclosure, in the context of developing countries where a dual board system is implemented.

Estimating the direct rebound effect for residential electricity use in seventeen European countries: Short and long-run perspectives

The energy saved by improving the building efficiency of European households may not be as much as expected due to a rebound effect. Based on Eurostat and World Bank annual panel data covering the period 1996–2020, this article develops dynamic panel approaches, a generalized method of moments, and first-difference specifications, to investigate the direct rebound effect of residential electricity consumption in selected European countries. Instrumental variable techniques are used to tackle the reverse causality of the electricity price in a demand model. Estimates of the direct rebound effect in residential electricity use are 18% in the short-run and 43% in the long-run. Our findings reject the hypothesis of a backfire effect in residential electricity demand. They have important implications for policy makers, suggesting the need for smart policies that consider energy consumption behaviors and decision-making processes among various households.

Bank ownership and credit risk: an empirical study of Indonesian Islamic local banks

Purpose This study aims to determine the effect of bank ownership on the credit risk of Indonesian Islamic local banks (ILBs). Design/methodology/approach This study uses the system generalized method of moments (GMM) estimation technique with a sample of 155 Islamic local banks in Indonesia from 2012 to 2019. Findings The results show that commissioner board (D.COW) ownership has a negative effect on credit risk. This indicates that an increase in the number of shares of Islamic local banks owned by the commissioner board reduces credit risk. On the other hand, government ownership (D.GOW), the Sharia supervisory board (D.SOW) and the director board (D.DOW) do not affect credit risk. Practical implications The government, Sharia supervisory board and director board need opportunities to easily own more Islamic local bank shares. Therefore, the provisions regarding the share ownership rights of the government, Sharia supervisory board and director board need to be improved to increase their role in reducing credit risk. Originality/value Previous researchers have not studied the effect of government ownership, the commissioner board, the Sharia supervisory board and the ownership of directors on credit risk at the ILB in Indonesia.

Modeling and simulation of bubble condensation using polydisperse approach with bubble collapse model

An accurate modeling of the bubble condensation phenomena in sub-cooled water is developed in this work, using a two-fluid model with one disperse phase and one Interfacial Area Transport Equation (IATE). For this, the aspects of some models are investigated, such as the choice of population balance models, Nusselt closure model and bubble collapse model in IATE. The standard method of moments is formulated using two different bubble size distribution functions among all, namely Dirac and quadratic laws. Therefore, different models for mass and energy interfacial transfers, interfacial forces and source terms of the IATE are developed using both functions. While limited differences are noticeable for the mass and energy transfers obtained by both functions, the results are largely improved using quadratic function for the drag force term and for the IATE source terms. Moreover, the simulations results show that the widely used Ranz-Marshall correlation clearly underestimates the condensation rate. However, this work shows that Chen–Mayinger correlation is relevant to simulate this type of flow, regardless the distribution function. Afterwards, a model is introduced to take into account the effect of bubble collapse by condensation in the IATE. The numerical results obtained using the quadratic function, the collapse model in the IATE and the Chen–Mayinger correlation are comparable to those obtained by the inhomogeneous MUltiple Size Group (iMUSIG) approach, while being less time-consuming.

Semiparametric spatial autoregressive models with nonlinear endogeneity

. This article constructs nonparametric two-step least squares (2SLS) and generalized method of moments (GMM) sieve estimators to estimate a functional-coefficient spatial autoregressive model with an endogenous environment variable. We derive the consistency and asymptotic normality results for our proposed sieve estimators. A small Monte Carlo study shows that our proposed estimators exhibit good finite-sample performance. An empirical application is used to illustrate the usefulness of our methods.

The role of green finance, environmental benefits, fintech development, and natural resource management in advancing sustainability

Green finance, generally considered as financial support for green growth, significantly reduces greenhouse gas and air pollutant emissions. Green finance in agriculture, green buildings and other green projects should increase the economic development of a country. Rapid advances in digital technology are revolutionizing the financial landscape. The rise of fintech has the potential to make financial systems more efficient and competitive and broaden financial inclusion. With greater technological complexity, however, fintech also brings potential systemic risks. Thus, using a panel dataset from the G7 and E7 countries, this investigation explores the interplay between green finance, environmental benefits, total natural resource management, fintech and economic sustainability. The panel dataset is subject to rigorous estimation techniques, including quantile regression and the generalized method of moments (GMM) for long-run estimation. The cointegrations among the variables are validated using the Johansen Fisher panel test. In the long-run estimation, the quantile regression shows significant influences of environmental benefits and total natural resource management. The GMM strengthens the analysis due to its effectiveness and reliability in the presence of heteroskedasticity. The outcomes of the GMM technique support the negative and significant impact of fintech, green finance and total natural resource management on GDP, while environmental benefits are significantly positively linked with GDP. The study enhances our understanding of the nexus between environmental factors and economic sustainability. Therefore, it has value in terms of its practical implications, offering policymakers insights for enhancing economic sustainability. Despite the significant contribution of the study, the limitations are acknowledged, which provide future researchers opportunities to expand on the topic.

Does the Too-Big-To-Fail status affect depositor’s discipline: a Gaussian mixture model algorithm approach

PurposeThis study empirically demonstrates a contradiction between pillar 3 of Basel norms III and the designation of Systemically Important Banks (SIBs), also known as Too Big to Fail (TBTF). The objective of this study is threefold, which has been approached in a phased manner. The first is to determine the systemic importance of the banks under study; second, to examine if market discipline exists at different levels of systemic importance of banks and lastly, to examine if the strength of market discipline varies at different levels of systemic importance.Design/methodology/approachThis study is based on all the public and private sector banks operating in the Indian banking sector. The Gaussian Mixture Model algorithm has been utilized to classify banks into distinct levels of systemic importance. Thereafter, market discipline has been observed by analyzing depositors' sentiments toward banks' risk (CAMEL indicators). The analysis has been performed by employing the system Generalized Method of Moments (GMM) to estimate models with different dependent variables.FindingsThe findings affirm the existence of market discipline across all levels of systemic importance. However, the strength of market discipline varies with the systemic importance of the banks, with weak market discipline being a negative externality of the SIBs designation.Originality/valueBy employing the Gaussian Mixture Model algorithm to develop a framework for categorizing banks on the basis of their systemic importance, this study is the first to go beyond the conventional method as outlined by the Reserve Bank of India (RBI).

The relationship between foreign direct investment and financial inclusion in MENA countries: Evidence from the General Method of Moments

The study delves into the nuanced interaction between foreign direct investment (FDI) and financial inclusion in the MENA region, spanning the years 2003 to 2022 and employing the General Method of Moments for rigorous analysis. Its primary objectives are to elucidate how financial inclusion influences FDI and to examine the mediating role of economic growth and inflation as key factors. Key findings reveal a robust positive correlation between financial inclusion and FDI inflows within MENA countries. Specifically, the study uncovers significant relationships between FDI and the various dimensions of financial inclusion, including access, availability, and usage. This underscores the pivotal role of inclusive financial systems in attracting foreign investment. Moreover, the study highlights the symbiotic relationship between economic growth and FDI, indicating that heightened levels of economic prosperity attract greater investment. This underscores the importance of fostering conducive economic conditions to attract foreign capital. Furthermore, the study underscores the critical role of financial inclusion in shaping monetary policy and mitigating investment risks. By facilitating access to capital and reducing uncertainty, financial inclusion promotes transparency and stability, thereby enhancing the attractiveness of foreign markets for investment.

Modelling for disability: How does artificial intelligence affect unemployment among people with disability? An empirical analysis of linear and nonlinear effects

There is a growing debate among scholars regarding the impact of artificial intelligence (AI) on the employment opportunities and professional development of people with disability. Although there has been an increasing body of empirical research on the topic, it has generally yielded conflicting findings. This study contributes to the ongoing debate by examining the linear and nonlinear effects of AI on the unemployment of people with disability in 40 countries between 2007 and 2021. Using the system Generalized Methods of Moments and panel smooth transition regression, the main conclusions are as follows. First, AI reduces the unemployment of people with disability in the full sample. Second, upon disaggregating the sample based on income level (high income/non-high income) and gender (men/women), the linear model only detects an inverse correlation between AI and unemployment among people with disability in high-income countries and among men, whereas it does not influence unemployment in non-high-income countries and women. Third, the panel smooth transition regression model suggests that the effects of AI on the unemployment of people with disability and among women are only observed once artificial intelligence interest search exceeds a specific threshold level. The effects of AI in non-high-income economies and among women are not significant in the lower regime, which confirms the nonlinear association between AI and the unemployment rate of people with disability. These findings have important policy implications for facilitating the integration of people with disability into the labor market.

Well-being and entertainment carbon efficiency: A study of spatial externality in geographic psycholinguistics in response to economic policy uncertainty

This study investigates the impact of well-being on carbon efficiency in the entertainment market from the perspective of geographic psycholinguistics. To analyze this impact, we employ quantile regression based on the method of moments and spatial econometric models that incorporate economic policy uncertainty. The results highlight the substantial impact of well-being on energy conservation and carbon emission reduction. On one hand, our findings indicate that well-being significantly reduces carbon emissions and exhibits a spatial externality effect. Furthermore, well-being indirectly contributes to carbon reduction by alleviating the adverse consequences of economic policy uncertainty. The rationale behind this relies on psycholinguistic expressions of well-being, which play a crucial role in enhancing the quality of life and serving as psychological foundations for individuals to adopt environmentally conscious actions and low-carbon lifestyles. This exploration is of great significance for understanding the synergistic growth of well-being and the development of the green economy.

5A accreditation of tourist attractions in China and international tourist arrivals: A system generalised method of moments approach

AbstractWhile international accreditations for tourist attractions have been extensively examined, the impact of nationally developed accreditations has been less systematically investigated. Employing the system generalised method of moments approach, this study investigates the relationship between 5A tourist attractions, based on China's accreditation system, and international tourist arrivals in China, using annual city‐level panel data spanning from 2002 to 2017. The empirical results suggest that the 5A accreditation has a significantly positive effect on international tourists' arrivals. Furthermore, our findings indicate that the 5A accreditation has a substantial impact on the number of international tourists visiting lesser‐known cities. Moreover, the study reveals that international tourists from different countries respond differently to cultural and natural sites. This research provides valuable insights into tourism development strategies for local governments and tourist attractions.

Enterprises’ Emissions Intensity and Financial Performance in Serbia: The Case Study of Wastewater

Untreated industrial and municipal wastewater is the key factor that leads to water pollution in Serbia. The aim of the paper is to examine the impact of environmental performance (using eco-intensity indicators) of enterprises on their profitability in the period 2011-2020 in Serbia in the area of wastewater. Apart from using the panel data technique in the paper, the Generalized Method of Moments (GMM) is also used to estimate the parameters in the model. The results demonstrate that if the eco-intensity indicator increases, the profitability of an enterprise increases significantly, whereas the profitability of an enterprise decreases with the increase in the size of an enterprise. According to the results of the evaluated model, the capital intensity variable has no influence on the profitability of an enterprise. Additionally, it has been determined that the coefficient with the eco-intensity indicator is quite large, indicating the poor eco-efficiency of Serbian enterprises in the area of wastewater.

Bayesian estimation of large-scale simulation models with Gaussian process regression surrogates

Large scale, computationally expensive simulation models pose a particular challenge when it comes to estimating their parameters from empirical data. Most simulation models do not possess closed-form expressions for their likelihood function, requiring the use of simulation-based inference, such as simulated method of moments, indirect inference, likelihood-free inference or approximate Bayesian computation. However, given the high computational requirements of large-scale models, it is often difficult to run these estimation methods, as they require more simulated runs that can feasibly be carried out. The aim is to address the problem by providing a full Bayesian estimation framework where the true but intractable likelihood function of the simulation model is replaced by one generated by a surrogate model trained on the limited simulated data. This is provided by a Linear Model of Coregionalization, where each latent variable is a sparse variational Gaussian process, chosen for its desirable convergence and consistency properties. The effectiveness of the approach is tested using both a simulated Bayesian computing analysis on a known data generating process, and an empirical application in which the free parameters of a computationally demanding agent-based model are estimated on US macroeconomic data.

How does governance quality affect the shadow economy-sustainable development nexus? New insights from a dynamic threshold analysis

Purpose The purpose of this study is to assess the impact of the shadow economy on three sustainable development indicators while considering the moderating effect of the governance quality, and to highlight the non-linearity of the considered relationship.Design/methodology/approach A sample of 82 countries covering the period from 1996 to 2017. The dynamic first-differenced generalized method of moments (FD-GMM) panel threshold model is implemented to control for non-linearity.Findings The shadow economy hinders sustainable development in countries with low-governance quality, while the opposite result holds in countries with high-governance quality. The critical thresholds triggering the switch from one regime to another vary across the sustainable development indicators. Boosting growth requires enhancing the legal system and the economic dimension of governance, while promoting environmental quality requires the implementation and enforcement of specific environment-friendly regulations.Originality/value The study addresses non-linearity and the moderating effect of governance quality. The use of six governance indicators allows to gauge the ability of each governance dimension to curb the negative effects of the shadow economy. Considering the three objectives of sustainable development allows to identify specific policy recommendations for each of them.

Impact of social welfare finance on institutional financial performance: Cross-country evidence

This study explores the nuanced impact of social welfare finance on institutional financial performance, focusing solely on its direct effects. Utilizing fixed effect, feasible generalized, and quantitative approaches alongside the dynamic generalized method of moments (GMM), we conduct a thorough analysis using data from 20 upper-middle-income economies spanning 2000–2023. Our findings highlight the pivotal role of social welfare finance, including its dynamic and lagged impacts, as a significant determinant of institutional financial performance. Quantile analysis reveals varying effects across different quantiles, enriching our understanding. The results emphasize the direct influence of social welfare finance and unveil its indirect impact through Corporate Social Responsibility (CSR), a novel contribution to the literature. Moreover, we stress the critical importance of reasonable resource allocation, highlighting the indispensable role of social welfare finance resources. We advocate for prioritizing public-private partnerships to optimize financial targeting, offering a pragmatic strategy for fostering a resilient financial economy.

Corporate governance and corporate carbon disclosures: The moderating role of earnings management

AbstractThis research aims to explore the relationship between corporate governance (CG) and carbon disclosure (CD) and the moderating role of earnings management (EM) in this relationship. The model was built based on legitimacy theory, upper echelons theory, and agency theory. We employed a two‐step generalized method of moments (GMM) regression and conducted robust tests to reaffirm the results using panel data from 134 listed companies in the Vietnamese stock market from 2015 to 2022. Our findings indicate that larger board sizes, boards with more independent members or the presence of a CEO who concurrently serves as the chairman of the board can potentially decrease the likelihood of CD, while companies with a higher proportion of female board members or regular board meetings may tend to publish more carbon information. Furthermore, EM can moderate the relationship between CG and CD, and this variable exhibits high reliability in the model. This research adds to the vast body of existing knowledge about the effectiveness of CG by investigating how different dimensions of CG affect corporate CDs in Vietnam, especially, to the best of the authors' knowledge, this is the first research attempting to provide the empirical result of the moderating role of EM in the relationship between CG and corporate CDs.

Estimation of Gumbel Distribution Based on Ordered Maximum Ranked Set Sampling with Unequal Samples

Sample selection is one of the most important factors in estimating the unknown parameters of distributions, as it saves time, saves effort, and gives the best results. One of the challenges is deciding on a suitable distribution estimate technique and adequate sample selection to provide the best results in comparison with earlier research. The method of moments (MOM) was decided on to estimate the unknown parameters of the Gumbel distribution, but with four changes in the sample selection, which were simple random sample (SRS), ranked set sampling (RSS), maximum ranked set sampling (MRSS), and ordered maximum ranked set sampling (OMRSS) techniques, due to small sample sizes. The MOM is a traditional method for estimation, but it is difficult to use when dealing with RSS modification. RSS modification techniques were used to improve the efficiency of the estimators based on a small sample size compared with the usual SRS estimator. A Monte Carlo simulation study was carried out to compare the estimates based on different sampling. Finally, two datasets were used to demonstrate the adaptability of the Gumbel distribution based on the different sampling techniques.

THE MODERATING ROLE OF IFRS ADOPTION SPEED IN THE RELATIONSHIP BETWEEN CAPITAL INFLOWS AND INCLUSIVE GROWTH IN SUB-SAHARAN AFRICA

Purpose: This study investigates how the speed of International Financial Reporting Standards (IFRS) adoption condition the capital inflows and inclusive growth nexus in Sub-Saharan Africa. While existing literature primarily examined IFRS impacts at the firm level, this study filles a significant gap by focusing on the macroeconomic effects of IFRS adoption speed, particularly its role in enhancing the benefits of capital inflows such as Foreign Direct Investment (FDI), Foreign Portfolio Investment (FPI), and Foreign Aid. Methods: This study employs a dynamic panel data analysis using the Generalized Method of Moments (GMM) to examine how the speed of IFRS adoption impacts capital inflows and fosters inclusive growth across Sub-Saharan African countries. The study leveraged a dataset spanning from 2005 to 2019, covering 48 countries in the region. By analysing the relationship between IFRS adoption speed, capital inflows, and inclusive growth, the study offers new insights into the macroeconomic benefits of adopting IFRS. Findings: Findings suggest that speedy IFRS adoption significantly enhances the positive impact of capital inflows on inclusive growth, underlining the importance of swift and effective implementation of IFRS for equitable economic development. Implications: This study contributes to the existing literature by providing a macroeconomic perspective on IFRS adoption and highlighting the importance of financial reporting standards in achieving inclusive growth in Sub-Saharan Africa. Originality: This study introduces a novel approach by examining how the speed of International Financial Reporting Standards (IFRS) adoption moderates the impact of capital inflows on inclusive growth in Sub-Saharan Africa. Bridging the gap in existing literature, it shifts the focus from firm-level effects of IFRS and direct impacts of capital inflows on economic growth to a nuanced exploration of how financial reporting standards' adoption speed can enhance a country's ability to utilize foreign investments for inclusive economic development. Unique in its regional focus, this research provides critical insights into the macroeconomic benefits of IFRS in Sub-Saharan Africa, offering valuable implications for policymakers and contributing significantly to the discourse on financial innovation, regulatory frameworks, and sustainable economic growth in emerging markets.