Moment Estimation Research Articles

Estimating Social Effects with Randomized and Observational Network Data

Abstract This paper introduces an innovative approach to identifying and estimating the parameters of interest in the widely recognized linear-in-means regression model under conditions where the initial randomization of peers determines the observed network. We assert that peers who are initially randomized do not produce social effects. However, after randomization, agents can endogenously develop significant connections that potentially generate peer influences. We present a moment condition that compiles local heterogeneous identifying information for all agents within the population. Under the assumption of ψ-dependence in the endogenous network space, we propose a Generalized Method of Moments (GMM) estimator, which is proven to be consistent, asymptotically normally distributed, and straightforward to implement using commonly available statistical software due to its closed-form expression. Monte Carlo simulations demonstrate the GMM estimator’s strong small-sample performance. An empirical analysis utilizing data from Hong Kong high school students reveals substantial positive spillover effects on math test scores among study partners in our sample, provided that their seatmates were exogenously assigned by their teachers.

Climate change and economic growth: Evidence for European countries

AbstractClimate change may affect economies and the welfare of people around the world. To design appropriate policy responses, the economic effects of climate change should be known. One strand in the literature empirically estimates the growth effects of climatic variations. However, those studies often neglect economic variables that have proven to be robust in explaining economic growth. Further, often they fail to check for the robustness of their results. The main aim of this study is to detect whether there exists a statistically significant robust relation between climate change and economic growth by estimating different model specifications. To do so panel estimation techniques for 24 European economies for the period from 2002 to 2019 are applied whereby panel fixed effects estimations and dynamic Generalized Methods of Moments estimations are resorted to. No statistically significant robust relationship between the temperature change and economic growth is found just as for precipitation that does not exert a significant effect on growth. As regards the institutional and macroeconomic control variables the rule of law, the fiscal variable, and the output gap are statistically significant and robust. It is argued that far‐reaching policy measures, such as the net zero goal of the European Union, should be given only based on robust results. Otherwise, economic policy may turn out to be inadequate and can lead to welfare losses. Hence, the conclusion is that the net zero goal of the European Green Deal is to be seen skeptical.

Evaluation of Four Forensic Investigative Genetic Genealogy Analysis Approaches with Decreased Numbers of SNPs and Increased Genotyping Errors

Background: Forensic investigative genetic genealogy (FIGG) has developed rapidly in recent years and is considered a novel tool for crime investigation. However, crime scene samples are often of low quality and quantity and are challenging to analyze. Deciding which approach should be used for kinship inference in forensic practice remains a troubling problem for investigators. Methods: In this study, we selected four popular approaches—KING, IBS, TRUFFLE, and GERMLINE—comprising one method of moment (MoM) estimator and three identical by descent (IBD) segment-based tools and compared their performance at varying numbers of SNPs and levels of genotyping errors using both simulated and real family data. We also explored the possibility of making robust kinship inferences for samples with ultra-high genotyping errors by integrating MoM and the IBD segment-based methods. Results: The results showed that decreasing the number of SNPs had little effect on kinship inference when no fewer than 164 K SNPs were used for all four approaches. However, as the number decreased further, decreased efficiency was observed for the three IBD segment-based methods. Genotyping errors also had a significant effect on kinship inference, especially when they exceeded 1%. In contrast, MoM was much more robust to genotyping errors. Furthermore, the combination of the MoM and the IBD segment-based methods showed a higher overall accuracy, indicating its potential to improve the tolerance to genotyping errors. Conclusions: In conclusion, this study shows that different approaches have unique characteristics and should be selected for different scenarios. More importantly, the integration of the MoM and the IBD segment-based methods can improve the robustness of kinship inference and has great potential for applications in forensic practice.

Automated lepidopteran pest developmental stages classification via transfer learning framework.

The maize crop is highly susceptible to damage caused by its primary pests, which poses considerable challenges in manually identifying and controlling them at various larval developmental stages. To mitigate this issue, we propose an automated classification system aimed at identifying the different larval developmental stages of 23 instars of 4 major lepidopteran pests: the Asian corn borer, Ostrinia furnacalis (Guenée; Lepidoptera: Crambidae), the fall armyworm, Spodoptera frugiperda (J.E. Smith; Lepidoptera: Noctuidae), the oriental armyworm, Mythimna separata (Walker; Lepidoptera: Noctuidae), and the tobacco cutworm, Spodoptera litura (Fabricius; Lepidoptera: Noctuidae). Employing 5 distinct Convolutional Neural Network architectures-Convnext, Densenet121, Efficientnetv2, Mobilenet, and Resnet-we aimed to automate the process of identifying these larval developmental stages. Each model underwent fine-tuning using 2 different optimizers: stochastic gradient descent with momentum and adaptive moment estimation (Adam). Among the array of models tested, Densenet121, coupled with the Adam optimizer, exhibited the highest classification accuracy, achieving an impressive 96.65%. The configuration performed well in identifying the larval development stages of all 4 pests, with precision, recall, and F1 score evaluation indicators reaching 98.71%, 98.66%, and 98.66%, respectively. Notably, the model was ultimately tested in a natural field environment, demonstrating that Adam_Densenet121 model achieved an accuracy of 90% in identifying the 23 instars of the 4 pests. The application of transfer learning methodology showcased its effectiveness in automating the identification of larval developmental stages, underscoring promising implications for precision-integrated pest management strategies in agriculture.

Uncertain Logistic Population Model with Allee Effect

By considering the influence of various uncertain noises, we established an uncertain Logistic population model with Allee effect, which was characterized by an uncertain differential equation. Firstly, we obtained the solution of the model, and discussed the stability of the equilibrium state. Secondly, the unknown parameters in the model were estimated by using the generalized moment estimation method under the framework of uncertainty theory. Finally, the parameter estimations of the model and the properties of the solution were explained by an example analysis.

Influence of silane and polyethylene glycol functionalization of boron nitride nanosheets on mechanical and thermal properties of epoxy nanocomposites: Machine and deep learning forecasting

AbstractThe influence of hexagonal boron nitride (hBN) nanosheet surface modification on epoxy nanocomposites' mechanical and thermal properties were investigated. An innovative approach was developed for synthesizing hBN nanosheets (hBNNs) via optimized ultrasonic‐assisted liquid‐phase exfoliation (UALPE), followed by functionalization with 3‐aminopropyltriethoxysilane (APTS) and polyethylene glycol (PEG600) for nanocomposite preparation. Compared to unmodified epoxy, nanocomposites containing 0.3 wt% of pristine hBNNs exhibited significant enhancements in flexural strength (125.28 MPa) and tensile strength (53.35 MPa), with respective increases of 40.57% and 43.23%. PEG‐hBNNs improved flexural and tensile strengths to 136.83 and 58.57 MPa, respectively. The most substantial enhancements were observed with APTS‐hBNNs, yielding flexural and tensile strengths of 174.13 and 63.53 MPa, reflecting increases of 95.37% and 70.50%, attributed to better interfacial interactions and a more effective crack deflection mechanism. Thermal stability analyses revealed superior performance of modified hBNNs epoxy nanocomposites, increasing 8.2°C and 6.3°C at 50 wt% degradation as compared to the unmodified system, owing to enhanced physical barrier properties. Structural and morphological analyses validated the successful exfoliation and modification of hBNNs. Predictive models utilizing machine and deep learning (DL) techniques, particularly DL with the adaptive moment estimation (ADAM) optimizer, effectively forecasted mechanical properties, achieving high R2 values.HIGHLIGHTS 0.3 wt% modified hBNNs epoxy nanocomposite showed the highest mechanical strength Improved thermal stability of epoxy nanocomposite by modification of hBNNs Improved crack deflection mechanism in the presence of surface‐modified hBNNs Deep learning with ADAM optimizer showed high R2 and low prediction loss

Using Political Connections to Raise Rivals’ Costs: Evidence from an Emerging Market

AbstractIn this paper, we leverage resource dependency theory to examine the impact of political connections on rivals’ costs and how this impact is affected by rival‐side and politician‐side contingencies. Using a panel dataset of Pakistani firms, our system generalized method of moments estimates show that politically connected firms can increase their rivals’ costs by influencing their rivals’ dependency relationship with the government. This impact is attenuated by the rivals’ strategic flexibility and social legitimacy, which provide some protection or insulation from political attacks in resource markets. Together, our findings extend the contingent dynamics of resource dependency in political markets from focal firms to competitors and offer significant contributions to the corporate political activity literature and resource dependency theory.

Exploring the nexus between exports’ economic complexity and institutional quality: insights from Italian provinces

ABSTRACT The present research aims to investigate the determinants of economic development and productivity by proposing an empirical analysis of the economic effects of institutional quality on the economic complexity of export at the intra-national level, focusing on 103 Italian provinces from 2004 to 2016. Using the System Generalised Method of Moments (System-GMM) estimator, the study assesses the influence of various institutional dimensions on provincial economic complexity, as measured by the Economic Complexity Index (ECI). The findings indicate that institutional quality, particularly rule of law, regulatory quality, and voice and accountability, significantly enhances economic complexity. Better formal and informal institutions such as completeness of contracts, social cohesion, contract enforcement, by reducing transaction costs and uncertainty, and by favouring knowledge dissemination and security of investments, are source of comparative advantage in complex industries that lead to diversified and sophisticated production, fostering economic growth and innovation. Moreover, the study emphasises the importance of related activities, which interact with institutions to support complex and innovative export-related activities through knowledge spillovers and related diversification. The analysis also highlights the pivotal role of human capital, social capital, and innovation, which should remain focal points for policymakers.

Multivariate copula-based conditional quantiles: analytic higher-order moments and ratio estimation approaches

ABSTRACT In this paper, we aim to modify multivariate copula-based conditional quantiles for a targeted random variable, given multiple random variables attaining their respective quantiles. Specifically, we propose two modifications through the Cornish–Fisher expansion, one of which involves its analytic higher-order unconditional moments. The second one accounts for its higher-order conditional moments estimated using a ratio estimation method. By considering multivariate elliptical and Archimedean copulas with Johnson's SU margins, our simulation study shows that this method provides more accurate conditional moment estimators compared to the naive method. They result in expanded conditional quantile estimators, whose efficiency is relatively better than their unexpanded versions, particularly at lower and higher quantile levels under a stronger (tail) dependence assumption. A much higher efficiency is gained when the first modification is performed. These findings are validated using an empirical study based on cryptocurrency return data and a comparative analysis against the existing estimation approaches.

ANALISIS SENTIMEN APLIKASI MICROSOFT TEAMS BERDASARKAN ULASAN GOOGLE PLAY STORE MENGGUNAKAN MODEL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENT ESTIMATION (ADAM)

The Covid-19 virus pandemic with its rapid spread has made the government enact several policies that can reduce community activities that have the potential to cause crowds including social distancing, work from home (WFH) and online and hybrid learning. One of the platforms that was widely used during the WFH and online learning period was Microsoft Teams. Microsoft Teams is an app that lets people and organizations collaborate and interact online. Microsoft Teams can be downloaded through the Google Play Store and users can leave reviews. Sentiment analysis is used to classify user sentiment towards Microsoft Teams into positive and negative sentiments. Review data obtained from January to November 2022. The classification is carried out using the Neural Network which is a computational system inspired by biological neural networks. Weight optimization is done using Adaptive Moment Estimation which is popular in the field of deep learning because it achieves good results quickly. The classification model in this paper was built with a training test ratio of 80:20. The best hyperparameter combination is node hidden layer 100, learning rate 0.0001, and batch size 32. The accuracy of the ANN classification model was obtained by 88.16%.Keywords: Microsoft Teams, Google Play Store, Sentiment Analysis, Neural Network, Adaptive Moment Estimation

Uncertainty quantification of collective nuclear observables from the chiral potential parametrization

Abstract We perform an uncertainty estimate of quadrupole moments and B(E2) transition rates that inform nuclear collectivity. In particular, we study the low-lying states of 6Li and 12C using the ab initio symmetry-adapted no-core shell model. For a narrow standard deviation of approximately 1% on the low-energy constants which parametrize high-precision chiral potentials, we find output standard deviations in the collective observables ranging from approximately 3-6%. The results mark the first step towards a rigorous uncertainty quantification of collectivity in nuclei that aims to account for all sources of uncertainty in ab initio descriptions of challenging collective and clustering observables.

Estimation of Electric Dipole Moment by Solvatochromism, Computational Method, and Study of the Effect of Solvents by Preferential Solvation of 6 - Methoxy-4-(4-Nitro-Phenoxy Methyl)-Chromen-2-One (6mnpm).

At room temperature, the absorption and fluorescence properties of coumarin 6-Methoxy-4-(4-nitro-phenoxy methyl)-chromen-2-one (6MNPM) are investigated in pure organic solvents and a combination of acetonitrile (ACN) and tetrahydrofuran (THF). The pure solvents' influence on spectral characteristics is examined by applying theories such as Kamlet and Catalan's multiple linear regression techniques, Reichardt's microscopic solvent polarity parameter, and the Lippert-Mataga polarity function. The significant role of solute-solvent interactions in pure solvents, particularly dielectric interaction and hydrogen bonding. However, hydrogen bonding interactions dominate the contribution of dielectric interactions. The electric dipole moments of both the ground as well as excited states had been calculated using the Solvatochromic method. The value of the excited state electric dipole moment and the redshifts of the emission spectra show that the emitting singlet state has an intramolecular charge transfer (ICT) character. From Catalan's linear regression, we found that di-polarity has a much smaller influence than polarizability. By solvation study, we conclude that Tetrahydrofuran solvent is preferred over Acetonitrile.

Quantifying the spillover effects of U.S. economic policy uncertainty on emerging market economies using GMM-PVAR model

This paper quantifies the spillover effects of economic policy uncertainty (EPU) in the United States on emerging market economies (EMEs). Using a generalized method of moments (GMM) estimation of a panel vector autoregression (PVAR) model on a dataset­ of 39 EMEs from 2005 to 2019, we find that increased U.S. EPU significantly raises the consumer price index (CPI) and negatively impacts the real GDP of these economies. Additionally, heightened U.S. EPU leads to a depreciation of emerging market currencies and a reduction in short-term interest rates. We employ a news-based EPU index developed by Baker et al. (2016) and conduct robustness checks using forward orthogonal transformation, an alternative EPU index, and by addressing the potential endogeneity of the oil price uncertainty (OPU) index. Our findings highlight the adverse effects of U.S. economic policy uncertainty on key macroeconomic variables in emerging markets, underscoring the importance of stable economic policies and robust institutions to mitigate these impacts.

Does financial expertise influence Islamic bank risk-taking?

PurposeThis study investigated how the financial expertise of the board of directors, risk management committee (RMC), audit committee (AC) and Shariá Supervisory Board (SSB) influences the risk-taking of Islamic banks.Design/methodology/approachThe study utilized unbalanced panel data for a sample of 43 full-fledged Islamic banks from 15 countries over 12 years (2010–2021). We employed feasible generalized least squares (FGLS) and heteroskedastic panels corrected standard errors (HPCSE) regression regressions as the primary estimation methods and used a two-step system generalized method of moments (GMM) estimator for robustness checks.FindingsThe results indicate that board financial expertise decreases insolvency and credit risks. Similarly, AC financial expertise and SSB financial expertise reduce insolvency and portfolio risks but increase credit risk. In contrast, RMC financial expertise raises insolvency risk. The remaining relationships are statistically insignificant.Research limitations/implicationsDue to the lack of disclosure regarding the educational and professional background of the board and committee members in the annual reports of some Islamic banks, this research used a sample of only 43 full-fledged Islamic banks operating in fifteen countries from 2010 to 2021.Practical implicationsThe findings can assist both local and international regulators in revising corporate governance codes and risk management guidelines in such a way as to ensure that the financial experts appointed to the board, AC, RMC and SSB, are capable of controlling excessive risk-taking behavior in Islamic banks.Originality/valueThis study contributes to the literature by providing comprehensive empirical evidence that corporate governance financial expertise influences the risk-taking behavior of Islamic banks.

A bivariate dependent degradation model based on artificial neural network supported stochastic process and Copula function

AbstractIn order to use the high ability of the artificial neural network (ANN) in data fitting, this paper introduces an ANN in stochastic process to describe the mean function for degradation modeling. Due to the fact that the existing method cannot handle the bivariate dependent degradation conditions, a bivariate dependent degradation model based on Copula function and ANN‐supported stochastic processes is proposed. Considering the random effects caused by individual difference, it is assumed that the unknown parameters in the stochastic processes and Copula functions are randomly distributed. Based on the maximum likelihood and moment estimation methods, a related statistical inference method for ANN training and parameter estimation is developed to use the bivariate dependent degradation model. An actual fatigue crack dataset is used to demonstrate the validity of the proposed method. The obtained results show that the dependent relationship between two degradation indicators should not be neglected, and it can be efficiently handled by the proposed method. Furthermore, the proposed degradation model can provide reliability and degradation intervals with enough precision due to the fact that it considers the random effects caused by individual difference.

On fractional moment estimation from polynomial chaos expansion

Fractional statistical moments are utilized for various tasks of uncertainty quantification, including the estimation of probability distributions. However, an estimation of fractional statistical moments of costly mathematical models by statistical sampling is challenging since it is typically not possible to create a large experimental design due to limitations in computing capacity. This paper presents a novel approach for the analytical estimation of fractional moments, directly from polynomial chaos expansions. Specifically, the first four statistical moments obtained from the deterministic coefficients of polynomial chaos expansion are used for an estimation of arbitrary fractional moments via Hölder’s inequality. The proposed approach is utilized for an estimation of statistical moments and probability distributions in four numerical examples of increasing complexity. Obtained results show that the proposed approach achieves a superior performance in estimating the distribution of the response, in comparison to a standard Latin hypercube sampling in the presented examples.

Measure-Theoretic Analysis of Stochastic Competence Sets and Dynamic Shapley Values in Banach Spaces

We develop a measure-theoretic framework for dynamic Shapley values in Banach spaces, extending classical cooperative game theory to continuous-time, infinite-dimensional settings. We prove the existence and uniqueness of strong solutions to stochastic differential equations modeling competence evolution in Banach spaces, establishing sample path continuity and moment estimates. The dynamic Shapley value is rigorously defined as a càdlàg stochastic process with an axiomatic characterization. We derive a martingale representation for this process and establish its asymptotic properties, including a strong law of large numbers and a functional central limit theorem under α-mixing conditions. This framework provides a rigorous basis for analyzing dynamic value attribution in abstract spaces, with potential applications to economic and game-theoretic models.

Estimation of joint moments during ball throwing in simulated altered gravity conditions using a musculoskeletal modelling approach: preliminary results

Estimation of joint moments during ball throwing in simulated altered gravity conditions using a musculoskeletal modelling approach: preliminary results

Electricity trade at exchanges of the world: Contextual analysis of Indian electricity exchanges

This study probes the relationship between price and traded volume in electricity exchanges worldwide. Deploying the generalised method of moments estimation on panel data from the day-ahead market of exchanges in 20 countries for the period 2015–2020, this study finds that an increase in the share of exchange-traded electricity in a country's total electricity consumption significantly reduces price. A critical analysis of the Indian electricity exchanges in this study reveals that even after 13 years of operations, consumers do not derive surplus as is expected from commodity exchanges.

Interpretable large-scale belief rule base for complex industrial systems modeling with expert knowledge and limited data

Complex system modeling technology is a hot topic. Nowadays, many complex industrial systems present three characteristics: multiple input indicators, limited data and interpretability requirements. With good interpretability, belief rule base (BRB) serves as an efficient tool for modeling complex systems. However, as the number of input indicators of industrial systems increases, BRB suffers from the combinatorial explosion problem, which makes it hard to generate large-scale BRB and optimize it while maintaining its interpretability. For this purpose, an interpretable large-scale BRB is proposed for complex systems with limited data, where expert knowledge can be utilized effectively. First, a framework for generating an initial large-scale BRB using expert knowledge and limited data is developed, including the determination of attribute weight, basic belief degree and rule weight. Afterwards, a new parameter optimization model is designed to reduce the burden of parameter optimization and maintain the interpretability of BRB, where the Adaptive Moment Estimation (Adam) algorithm is adopted to further improve the efficiency of large-scale parameter optimization. Finally, a health assessment case of an inertial navigation system (INS) verifies the proposed method.