Method Of Moments Estimator Research Articles

A comparative study on ensemble soft-computing methods for geothermal power production potential forecasting

Many developed countries are increasingly interested in renewable energy sources (RESs) as a result of environmental changes and the depletion of fossil fuels in recent years. Since geothermal energy can be used as both a source of electricity and heat, it occupies an important spot among renewable energy sources. In this study, soft-computing ensemble models (SCEMs) based on supervised deep neural network (SDNN) models supported by the forward stepwise regression (FSR) method are used in estimating the power generation from geothermal resources. Outputs of the FSR process led SDNN phase. Adaptive Moment Estimation (ADAM) and Nesterov-accelerated Adaptive Moment Estimation (NADAM) methods were used to optimize SDNN models. For the daily power generation, the best performance has been shown by the model of SDNN optimized using ADAM optimizer with a coefficient of determination (R2) of 0.9807 and root mean square error (RMSE) of 0.0466, respectively.

Liquidity Risk Exposure and Earnings Management—Results on an Unexplored Relation in the European Banking Sector

ABSTRACT This study investigates the relation between liquidity risk exposure and earnings management in banks. We use a dynamic panel-data one-step system generalized method of moments estimation and measure exposure to liquidity risk from balance sheet positions. We detect earnings management through income-increasing and -decreasing strategies. We find that the higher the exposure to liquidity risk due to higher liquidity creation, the greater is upward earnings management. Our findings suggest that banks use earnings management to opportunistically conceal high liquidity risk exposure. These findings should be useful for investors and users of financial statements interested in analyzing earnings quality and financial statement reliability. Data Availability: Data are available from specific sources cited in the text. JEL Classification: G21; G30; G32; M41.

Does ownership structure drive the effect of CEO overconfidence on earnings quality?

PurposeThe purpose of this paper is to examine the effect of chief executive officer (CEO) overconfidence on earnings quality and the moderating role of ownership structure as a crucial corporate governance device.Design/methodology/approachThe paper uses the generalized method of moments (GMM) estimation method to test our models on a sample of 335 French companies between 2009 and 2020, i.e. 4,020 observations.FindingsThe results show that CEO overconfidence negatively affects earnings quality. This result supports the predictions of behavioral finance theory and suggests that CEO overconfidence is a behavioral bias that affects the quality of earnings. The authors also examined the effect of different types of ownership structures on this relationship. The results show the significant role of controlling shareholders, owner-managers, families and institutional investors in mitigating the negative effect of CEO overconfidence on earnings quality.Research limitations/implicationsThis paper has some limitations. First, other types of ownership structures could have been analyzed such as state ownership. Second, we ignored the role of the board of directors as an important governance mechanism in controlling overconfident CEOs’ actions.Practical implicationsCompanies should be aware of the potential risks associated with CEO overconfidence, which can compromise the faithful representation of earnings. This highlights the importance of effective monitoring and internal controls to detect and prevent such practices, which involve the role of ownership structure.Originality/valueThis paper addresses the effect of CEO overconfidence on earnings quality and provides new evidence on the role of different ownership structure types in shaping this relationship. Additionally, this paper sheds new light on how overconfident CEOs may behave in challenging times.

How do natural resource rents and productive capacity affect carbon emissions? Evidence from developed and developing countries

This study re-examines the determinants of CO2 emissions, considering 36 developing and developed economies from 2000 to 2020. The study uses a new indicator of economic cycles, i.e., the productive capacity index. The panel models also control for the impacts of geopolitical risk, globalisation, renewable energy share, and total natural resource rents. After diagnosing the data for necessary panel diagnostics, the Westerlund cointegration test indicates a significant long-run relationship. In this context, two-step System Generalised Method of Moments (SGMM) estimations demonstrate the positive effects of geopolitical risk, productive capacity index, and natural resource rents on per capita CO2 emissions. In contrast, globalisation and the share of renewable energy reduce CO2 emissions. The Panel-Correlated Standard Errors (PCSE) and the Feasible Generalised Least Squares (FGLS) confirm the long-run reliability of these baseline findings. Policy implications concerning the issue of rising CO2 emissions are also discussed.

Analisis Determinan Struktur Modal pada Perusahaan Property dan Real Estate

The aim of this research is to determine the influence of asset tangibility, liquidity, non-debt tax shield, business risk, growth and firm size on the capital structure of property and real estate sector companies listed on the Indonesia Stock Exchange in 2014-2021. A dynamic panel data regression model with the Generalized Method of Moment (GMM) estimator is applied in this research. The results show that determinants simultaneously influence capital structure. Asset tangibility, growth and firm size partially have a significant effect on capital structure, while liquidity, non-debt tax shield and business risk partially have no effect on capital structure. Therefore, the formation of an optimal capital structure is expected to take into account tangible assets, growth rate and company size in order to maximize company value.

Oil price volatility and changes in corporate debt: An empirical study in the Indian landscape

Changes in crude oil prices affect corporate debt position. This study investigates the association between changes in debt and crude oil price volatility in nonfinancial, BG, and SA firms in India. We use financial variables, crude oil prices, and macroeconomics data from 2004 to 2020 for 16,452 firm-years and this study employs unbalanced panel data and utilizes different empirical methodologies, including the generalised method of moments (GMM) estimator, to test the stated hypotheses. The results reveal that the debt position and the crude oil price volatility significantly have different effects on business group (BG) and stand-alone (SA) firms. Crude oil price volatility is positively related to changes in the debt position. The effects of the financial variables also significantly differ for the BG and SA firms. Finally, firms change their debt position in the positive or negative direction based on the fluctuations in crude oil prices. Policy implications have also been provided.

From black to green: Quantifying the impact of economic growth, resource management, and green technologies on CO2 emissions

In an exploration of environmental concerns, this groundbreaking research delves into the relationship between GDP per capita, coal rents, forest rents, mineral rents, oil rents, natural gas rents, fossil fuels, renewables, environmental tax and environment-related technologies on CO2 emissions in 30 highly emitting countries from 1995 to 2021 using instrumental-variables regression Two-Stage least squares (IV-2SLS) regression and two-step system generalized method of moments (GMM) estimates. Our results indicate a significant positive relationship between economic growth and CO2 emissions across all quantiles, showcasing an EKC with diminishing marginal effects. Coal rents exhibit a statistically significant negative relationship with emissions, particularly in higher quantiles, and mineral rents show a negative association with CO2 emissions in lower and middle quantiles, reinforcing the idea of resource management in emissions reduction. Fossil fuels exert a considerable adverse impact on emissions, with a rising effect in progressive quantiles. Conversely, renewable energy significantly curtails CO2 emissions, with higher impacts in lower quantiles. Environmental tax also mitigates CO2 emissions. Environment-related technologies play a pivotal role in emission reduction, particularly in lower and middle quantiles, emphasizing the need for innovative solutions. These findings provide valuable insights for policymakers, highlighting the importance of tailoring interventions to different emission levels and leveraging diverse strategies for sustainable development.

Artificial intelligence and socioeconomic forces: transforming the landscape of religion

This research examines the influences of artificial intelligence and socioeconomic factors on religious freedom across 20 countries from 2000 to 2022. Employing a detailed model that integrates both specific effects related to individual countries and annual changes, our study offers an extensive empirical analysis of how artificial intelligence relates to religious freedom. The results indicate a notable negative impact of artificial intelligence on religious freedom. Furthermore, the study sheds light on key factors that affect religious freedom, uncovering a positive correlation with elements such as economic growth, political stability, and education levels. However, it was also observed that increased digitalization correlates negatively with religious freedom. These conclusions are reinforced by findings from the system-generalized method of moment estimation, which strongly support our initial results. Consequently, this study establishes that the relationship between artificial intelligence and religious freedom is intricate and shaped by a variety of socioeconomic factors. Our findings emphasize the necessity for thoughtful consideration of the broader societal impacts of artificial intelligence, especially regarding essential human rights like religious freedom.

The impact of digital governance on tourism development

This paper examines the impact of digital governance on inbound tourism using the panel dataset from 151 countries from 2000 to 2019. Fixed-effects and System Generalised Method of Moments estimations show that the internet shutdown capacity and practice decrease inbound tourism. Social media monitoring with strict rules also hinders inbound tourism. It is found that governance capacity and practical censorship implications negatively affect tourism activities. Several policy implications for the proper control of digital communications are also discussed.

Effects of female CEO and female CFO on internationalization and firm performance

PurposeThe study aims to examine the influence of female chief executive officer (CEO) and female chief financial officer (CFO) on the linkage between internationalization and firm performance.Design/methodology/approachThis study used 2926 firm-year observations of nonfinancial firms listed on the Pakistan Stock Exchange over the period 2012–2021. This study used ordinary least squares regression method to test the hypotheses, and additionally, generalized method of moments estimation and fixed effect analysis were used to check for the robustness of the results.FindingsUsing the framework of upper echelons theory and resource dependence theory, this study reports that internationalization has a positive impact on firm performance. Moreover, the results show that the presence of female CEO and female CFO strengthens the positive relationship between internationalization and firm performance. The results add to the gender diversity literature by highlighting the positive role of female CEOs and female CFOs on the internationalization and performance of firms in a male-dominated society.Originality/valueThis study adds to the limited literature on the internationalization of businesses in an emerging market and provides empirical support to upper echelons theory and resource dependence theory by highlighting the benefits brought to the firm through female CEOs and female CFOs.

Carbon option pricing based on uncertain fractional differential equation: A binomial tree approach

In this paper, we use a Caputo–Hadamard uncertain fractional differential equation (UFDE) to describe the change of carbon emission rights price. Based on a binomial tree approach, a portfolio is constructed by buying options and shorting the underlying assets in a certain proportion. The pricing formulas for carbon finance European option are presented, and the algorithms are designed to formulate the price of carbon finance American option without explicit pricing formulas. Besides, a moment estimation method is used to obtain the parameter values in Caputo–Hadamard UFDE, and a method is provided to simulate the observation data of solution of Caputo–Hadamard UFDE. Finally, the specific Caputo–Hadamard UFDE with mean-reverting process is applied to simulating the change of carbon emission rights price in Chinese carbon market, and the feasibility of the proposed carbon option pricing method is illustrated by some numerical examples.

Volatility estimation of hidden Markov processes and adaptive filtration

The partially observed linear Gaussian system of stochastic differential equations with low noise in observations is considered. The coefficients of this system are supposed to depend on some unknown parameter. The problem of estimation of these parameters is considered and the possibility of the approximation of the filtering equations is discussed. An estimators are used for estimation of the quadratic variation of the derivative of the limit of the observed process. Then this estimator is used for nonparametric estimation of the integral of the square of volatility of unobservable component. This estimator is also used for construction of method of moments estimators in the case where the drift in observable component and the volatility of the state component depend on some unknown parameter. Then this method of moments estimator and Fisher-score device allow us to introduce the One-step MLE-process and adaptive Kalman–Bucy filter. The asymptotic efficiency of the proposed filter is discussed.

CEO attributes and borrowing costs: exploring the moderating role of financial literacy

ABSTRACT This study examines the effect of Chief Executive Officer (CEO) attributes on borrowing costs and the moderating role of the CEO’s financial literacy on this relationship. We employed 2926 firm-year observations of non-financial firms listed on the Pakistan Stock Exchange. We used ordinary least squares regression to test the hypotheses and further employed fixed effect analysis, generalized method of moments estimation and two-stage least squares analysis to resolve the possible endogeneity concerns. Our empirical results indicate that CEO age, tenure, ownership and gender are negatively associated with borrowing costs, whereas CEO duality is positively associated with borrowing costs. Furthermore, we report that a financially literate CEO strengthens the relationships in the cases of age, tenure, ownership and gender while it weakens the relationship in the case of CEO duality. Our results provide novel evidence in this context and extend empirical support to upper echelons theory in the case of an emerging economy.

Digital financial inclusion, international remittances, and poverty reduction

Ensuring access to and usage of formal financial services through digital devices can be referred to as “digital financial inclusion” (DFI). In recent years, there has been a growing trend in the use of financial services, including money transfers through mobile phones. This study applies mobile phone subscriptions as a proxy to measure the degree of DFI and explores the individual effects of DFI and remittances and their interaction effects on poverty conditions in developing countries. Using panel data from 2000 to 2020 for 123 countries and employing the dynamic generalized method of moments estimation, the results reveal that DFI and remittance inflows help ameliorate poverty in developing countries. Furthermore, we find that the coefficient of the interaction term between DFI and remittances is statistically significant and positive, suggesting that the impact of DFI on poverty alleviation could weaken as remittance inflows increase in the remittance-receiving country and vice versa.

Human Capital Efficiency and Firm Performance of Quality-Certified Firms from EFQM Excellence Model: A Dynamic Panel Data Study

Abstract Purpose The study’s main purpose is to investigate the impacts of human capital efficiency (HCE) on the firm performance of non-certified and quality-certified firms from the European Foundation for Quality Management (EFQM) Excellence Model. The study also examines the relationship between firm performance and quality-certificates from the European Foundation. Design/methodology/approach By using a sample of 282 non-certified and 22 quality-certified firms from 2017 to 2021, the current study employed a two-step system generalized method of moments (GMM) estimation to analyse the empirical data. The dummy variable is used to examine the relationship between quality-certificates and firm performance. The dummy interaction term is employed to quantify the impacts of HCE on the firm’s performance for quality-certified firms. Findings The results revealed that quality-certificates and firm performance have a positive relationship. Quality-certified firms perform better and earn more profits than non-certified firms. HCE has a positive impact on firm performance for both types of firms. Moreover, the quality-certified firms utilize HCE in an efficient way to earn more profits compared to the non-certified firms. Originality This is the first study to use a comprehensive analysis to emphasize the HCE for non-certified and quality-certified firms separately. The effects of quality-certificates on firm performance in the context of HCE are also being highlighted for the first time in this research. Practical implications The current study’s findings are fruitful for academics, managers, researchers, policymakers, and other firm management. The findings will encourage the management of the firms to implement the total quality management (TQM) approach within their firms.

Economic policy uncertainty and financial system efficiency

Economic policy uncertainty (EPU) adversely affects financial system functioning with potentially critical repercussions for economies and corporations worldwide. Financial system efficiency (FSE) has a vital influence on fostering optimal economic growth and development; however, the impact of EPU on FSE remains under-explored. This study investigates the effect of EPU on FSE along with its components financial institution efficiency (FIE) and financial market efficiency (FME). Using data from 22 countries over a 20-year period (2002–2021), our analysis reveals a significant negative effect of EPU on FSE, FIE and FME. Notably, our split-sample analysis highlights the accentuated adverse effects of EPU in high-EPU regimes, emphasising the importance of vigilance during periods of elevated policy uncertainty. We conduct a series of sensitivity tests, including alternative measures of EPU, FSE, FIE and FME, and apply two-stage least squares and two-step dynamic system generalised method of moments estimators and introduce additional control variables. These tests consistently reaffirm the core conclusions of our study. Finally, we discuss the implications of our findings for policymakers.

Location choice of foreign direct investment in technical KIBS in China: impact of human capital and intellectual property rights protection

PurposeThis study considers the “technology creation” characteristic of technical knowledge-intensive business services (T-KIBS) and examines how human capital and intellectual property rights (IPR) protection affect the location choice of foreign direct investment (FDI) in China for two types of T-KIBS: (1) information transmission, software and information technology (ICT) services and (2) scientific research and technology (SCI) services.Design/methodology/approachOur empirical analysis is based on panel data on 22 Chinese provinces from 2009 to 2017. We use the generalized method of moments estimation for the regression analysis.FindingsFDI in ICT services prefers regions with high human capital, while FDI in SCI services favors regions with good IPR protection.Research limitations/implicationsFuture research could use more comprehensive data and qualitative interviews to enhance the findings.Practical implicationsThese findings provide a foundation for China’s future policy on attracting FDI into T-KIBS, especially in areas related to human capital and IPR protection.Originality/valueThis study bridges the research gap on the FDI location choice of T-KIBS in China by clarifying the influences of human capital and IPR protection and providing theoretical support for the location choice of T-KIBS FDI.

Does skill emigration hurt unskilled workers? Theory and cross‐country evidence

AbstractHow does out‐migration of skilled workers affect unskilled workers' wage in the source country? When skilled workers emigrate, unskilled wages tend to go down in some countries. If the sector that uses both skilled and unskilled workers shows a lower degree of capital intensity as compared to sectors that use only skilled workers in production, it is a common outcome. We use 19 years of cross‐country data from the International Labor Organization (ILO) spanning Asia and Latin America to show that skill emigration reduces unskilled wage unambiguously for panel fixed effects and difference generalized method of moments (DGMM) estimates. The structure is also subjected to system GMM with endogenous covariates and allied robustness checks. Importantly, we find a critical level of tertiary education, such that countries generating more skill shall face weaker negative impact on unskilled wages.

Exploring the connections: Dividend announcements, stock market returns, and major sporting events

This study conducts a detailed investigation into the interplay between major sporting events, specifically the ICC Cricket World Cups and FIFA Football World Cups, and their potential impact on the relationship between dividend announcements and stock market returns. Beyond the customary exploration of investor sentiment and its connection to stock market returns, our research thoroughly examines the effects of these significant sports events on the stock market's reaction to dividend announcements. Drawing on extensive FTSE 350 index data spanning January 1990 to December 2021, we employ event study methodology as the primary analytical framework. To bolster the reliability of our findings, we apply the Generalized Method of Moments (GMM) estimation method, addressing potential endogeneity concerns. Our results uncover a distinct pattern—the stock market exhibits a less favourable response to dividend increases announced following England's victories in major sporting events, such as the FIFA Football World Cup and ICC Cricket World Cup, compared to instances where they faced defeat. Additionally, we observe a more negative market response to dividend decreases announced following England's losses in these pivotal sporting events, as opposed to England emerging victorious in these key contests. This research contributes valuable insights into the intricate relationship between sports passion and market dynamics, offering implications for both scholarly discourse and investment strategy formulation.

Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data

This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n , T → ∞ , and the strength of the dominant units is equal to 1 when T → ∞ . For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.