Generalized Moments Research Articles

THE EFFECTS OF INTANGIBLE ASSETS ON COMPANY PERFORMANCE IN CRISIS PERIOD: THE CASE OF TURKEY

Despite the lack of physical form, intangible fixed assets can yield unique, identifiable advantages, crucial in goods production and service provision. During periods of economic upheaval, these non-physical investments demonstrated resilience, even exhibiting growth in some contexts, thus featuring their economic significance. This study scrutinizes the impact of intangible fixed assets on firm profitability and financial performance under varying economic conditions and within existing accounting policies. Using Dynamic Panel Data Analysis and the Arellano and Bover/Blundel and Bond System Generalized Moments Method Estimator, the study offers pertinent recommendations. Our findings confirm that intangible fixed assets positively influence return on equity and net profit margin during crisis periods.

Empirical Analysis of the Role of Population Mutations on Agricultural Productivity in Sub-Saharan Africa

This paper aims to analyze the role of population growth in the explanation of agricultural productivity in the context of Sub-Saharan African countries. Based on available data, we collected data on a sample of 24 Sub-Saharan African countries over the period 2000-2018. The empirical method consists of estimating an agricultural production function in turn by Generalized Least Squares (GMM) estimators on panel data and Generalized Moments (GLS) estimators in system. The results suggest that pesticide use is positively and significantly correlated (at the 1% threshold) with agricultural productivity in SSA. They also show that life expectancy at birth positively and significantly explain agricultural productivity in SSA. This means that the larger the population, the more important agricultural productivity is. The rural population is negatively correlated with agricultural productivity, reducing agricultural employment as rural populations migrate in search of better living conditions. We therefore suggest that African governments step up financial support programs for young people to limit the brain drain and encourage agricultural employment.

Fragility of FDI flows in sub-Saharan Africa region: does the paradox persist?

The circumstances of the SSA region regarding the inflow of foreign direct investment (FDI) present a puzzle. In spite of the high rate of return on investment, the inflow of foreign investments keeps eluding the region, and the COVID-19 pandemic even perplexes the flow fragility the more. What factors then determine FDI flows aside from return on investment? Could there be more persuasive relative cost complexes? The study aimed at testing the effects of determining factors that influence FDI flows and their impact on economic development, considering the COVID-19 period. The study used cross-country pooled data from 30 SSA countries collected between 2001 and 2020. The study utilized five panel estimation techniques, namely Pooled Regression, Fixed Effect (FE), Random Effect (RE), Panel Two-Stage Least Square and Differenced Generalized Moments of Method (DGMM). The study found that the inflow of FDI has significant positive impact on economic development in the sub-Saharan African region. It is also ascertained that the outflow of FDI, and political stability has an inverse relationship with economic development. The study recommends that governments of host economies should hence ensure an enabling framework for their economies, so as to improve infrastructure, political stability, and institutional quality, in order to sufficiently encourage the inflow of FDI into the SSA region and make the environment inviting, sustainable, and beneficial for foreign investors and host economies alike.

Distributed Consensus-Based Online Monitoring of Robot Swarms With Temporal Logic Specifications

In this letter, we develop a distributed consensus-based online monitoring framework for a robot swarm with a fixed graph structure. Each agent can monitor whether the swarm satisfies specifications given in the form of Swarm Signal Temporal Logic (SwarmSTL) formulas. SwarmSTL formulas describe temporal properties of swarm-level features represented by generalized moments (GMs), e.g., centroid and variance. To deal with measurement noise, we propose a generalized moment consensus algorithm (GMCA) with Kalman filter (KF), allowing each agent to estimate the GMs. Besides, we prove the convergence properties of the GMCA and derive an upper bound for the error between an agent's estimate of the GMs and the actual GMs. This upper bound is derived to be dependent on the maximal allowed velocity but independent of the agents' exact motion. A set of distributed monitoring rules for SwarmSTL formulas are proposed based on the estimation error bound. As a result, the agents can monitor the satisfaction of SwarmSTL formulas over swarm features during execution. The distributed monitoring framework is applied to a supply transportation example, where the efficacy of KF in the GMCA is also shown.

Understanding the Performance of the Banking System in Times of Crisis: the Case of Commercial Banks in the CEMAC

This article aims to explain the performance of the banking system in the Central African Economic and Monetary Community (CEMAC) during a crisis period by exploring the average value of the bank profitability indicator. A comparative reading of this indicator between 1986 and 2020 shows that profitability during a crisis period is higher than during the normal period. Using an analysis of the model of banking performance with the method of generalized moments (GMM), the results show that the contradiction relating to the banking performance is explained by: 1)- the strong presence of the State in the capital of commercial banks in the CMEAC zone during the crisis; 2)- the decline in liquidity characteristic of crisis periods also explains this performance of the CEMAC banking sector. It is recommended that the monetary authorities ensure that their decisions are strictly applied by commercial banks in normal times as well as in times of crisis

Finansal Türevler ve Banka Riski: D-8 Ülkeleri Örneği

Purpose: The study aims to investigate the impact of financial derivatives use on bank risk. For this purpose, the banks operating in D-8 countries, are investigated. 
 Design/Methodology: While the relationship between the bank risk and derivative utilize is examined, some of the bank-specific control variables are added to the model. Due to the sample structure and endogeneity problem in the study, hypotheses were tested again by using Generalized Moments Method.
 Findings: The analysis results showed a statistically significant and negative relationship between financial derivative use and bank risk and using financial derivatives is reduced the bank's risk.
 Limitations: From D-8 countries, Iran and Bangladesh were excluded from the study because of the inaccessibility of the data related to the financial derivatives.
 Originality/Value: While the relationship between "Banking Risk and Use of Derivative Financial Instruments" in the literature is mainly tested in developed countries, there is a gap in the literature on how this relationship is in developing countries. This study aims to contribute to the literature by revealing the relationship between the use of financial derivatives and bank risk in D-8 countries, which are classified as developing countries.

DOES OIL DEPENDENCE AFFECT REGIONAL WEALTH? A REGIONAL STUDY FOR THE MUNICIPALITIES OF THE STATE OF RIO DE JANEIRO

The aim of this study is to analyze the regional effects of oil royalty dependence on the local economic development of municipalities of the Rio de Janeiro state. To do so, we used spatial econometric models to estimate the local and spillover effects of royalty dependence on the economic local product. The main results were derived based on the Spatial Autoregressive Combined model (SAC) estimated by Generalized Moments (GM). The study's contribution is to address the natural resource curse debate through its regional perspective by estimating the direct, spillover, and total effects of oil royalty dependence on economic product, which is a groundbreaking approach for the natural resource curse debate. The results show that both the direct and spillover effects are negative and significant, indicating that there is evidence of the presence of a regional natural resource curse in the municipalities of the Rio de Janeiro state.Keywords: Spatial Econometrics; Natural Resource Curse; Econometrics; Spillover effects; Spatial Panel Data Models; Method of Generalized MomentsJEL Classifications: C33, E69, N56.DOI: https://doi.org/10.32479/ijeep.11737

Financial globalization, institutions and economic growth impact on financial sector development in fragile countries using GMM estimator

Purpose- The aim of the research is to predict the impact of financial globalization, institutional quality and economic growth on financial development in fragile economies. In this paper the panel data consists of Turkey, Brazil, India, South Africa, Indonesia, Argentina, Egypt, Pakistan’s annual data from 1995-2017. Methodology –System GMM dynamic panel data approach has been applied to deal with simultaneity bias and endogeneity bias when the explanatory variable is correlated with the residual disturbance term. The System GMM estimator combines regression in differences with regression in levels to get rid of the individual specific effects and along with it any time invariant regressor. The models are estimated by using one step system GMM estimator in other words Arellano and Bover /Blundell and Bond System Generalized Moments Method. Findings- The results show that economic growth and financial development are positively related. Thanks to financial development interest rates can be determined by market conditions and financial intermediaries can minimize transaction costs and information acquisition costs can be minimized. Empirical findings suggest policy guidelines for developing financial sector by using economic growth as an economic instrument. Conclusion- The paper concludes that economic growth have significant impact on financial development so both financial institutions and financial markets development in fragile countries. For less developed countries, developments in institutions are likely to have far greater direct effects on growth than financial development itself. When the financial system is developed, Institutional improvements can also deliver more growth. Since global standards for institutions such as International Country Risk Guide, Global Government Indicators increase, it seems also developing ountries are aware of the importance of institutional quality on economic growth. The findings suggest that financial development is affected by economic growth, inflation and population in fragile countries.

The Incidence of Economic Freedom and Foreign Direct Investment in Economic Growth: Empirical Evidence from Tunisia

Economic freedom is one of the most important factors in increasing economic growth. Thus, it affects the spillover effects of FDI through the improvement of the absorption capacity in the host countries. In this context, this thesis aims to study the macroeconomic impact of economic freedom on foreign direct investment inflows by basing empirical evidence for data from Tunisia over a period from 1980 to 2017. Using the famous economic method of generalized moments (GMM), the results of the estimates show that FDI positively affects economic growth and requires a high level of economic freedom, and makes it possible to increase the absorption capacity in host countries and '' attract more investors. Economic freedom increases FDI inflows to 0.098 units. More precisely, Tunisia also refers to the positive impact of economic freedom on FDI.

A new fast algorithm to compute moment 3D invariants of generalized Laguerre modified by fractional-order for pattern recognition

Orthogonal moments are the projections of image functions on particular functions of the kernel. They play an essential role in image extraction: rotation, scaling, translation invariance, object recognition, image classification, image noise robustness, and low information redundancy. These moments are derived from orthogonal polynomials that can be continuous or discrete. This paper focuses on the fractional-order modified generalized Laguerre moment invariants (FMGLMIs), which is a generalization of the traditional integer order one. In this research, we have developed a new algorithm to compute the 3D invariant moments of FMGLMIs based on the 3D image cuboid representation, our proposed calculation method can improve the efficiency of 3D invariant moment calculation to maintain numerical stability and significantly reduce calculation time with very satisfactory accuracy. To check this new algorithm, the calculation of 3D invariant moments gives very encouraging results for the invariability property of the proposed method with respect to different geometric transformations and noise degradations of 3D images, classification and recognition of 3D images and the calculation time of fractional-order invariants proposed. Finally, the experimental results show that the proposed method makes it possible to construct fractional-order modified generalized Laguerre invariant moments offering better performances for image analysis and pattern recognition.

Generalized Method of Moments Estimators for Multiple Treatment Effects Using Observational Data from Complex Surveys

AbstractIn this article, we consider a generalized method moments (GMM) estimator to estimate treatment effects defined through estimation equations using an observational data set from a complex survey. We demonstrate that the proposed estimator, which incorporates both sampling probabilities and semiparametrically estimated self-selection probabilities, gives consistent estimates of treatment effects. The asymptotic normality of the proposed estimator is established in the finite population framework, and its variance estimation is discussed. In simulations, we evaluate our proposed estimator and its variance estimator based on the asymptotic distribution. We also apply the method to estimate the effects of different choices of health insurance types on healthcare spending using data from the Chinese General Social Survey. The results from our simulations and the empirical study show that ignoring the sampling design weights might lead to misleading conclusions.

Multilacunarity as a spatial multiscale multi-mass morphometric of change in the meso-architecture of plant parenchyma tissue.

The lacunarity index (monolacunarity) averages the behavior of variable size structures in a binary image. The generalized lacunarity concept (multilacunarity) on the basis of generalized distribution moments is an appealing model that can account for differences in the mass content at different scales. The model was tested previously on natural images [J. Vernon-Carter et al., Physica A 388, 4305 (2009)]. Here, the computational aspects of multilacunarity are validated using synthetic binary images that consist of random maps, spatial stochastic patterns, patterns with circular or polygonal elements, and a plane fractal. Furthermore, monolacunarity and detrended fluctuation analysis were employed to quantify the mesostructural changes in the intercellular air spaces of frozen-thawed parenchymatous tissue of pome fruit [N. A. Valous et al., J. Appl. Phys. 115, 064901 (2014)]. Here, the aim is to further examine the coherence of the multilacunarity model for quantifying the mesostructural changes in the intercellular air spaces of parenchymatous tissue of pome and stone fruit, acquired with X-ray microcomputed tomography, after storage and ripening, respectively. The multilacunarity morphometric is a multiscale multi-mass fingerprint of spatial pattern composition, assisting the exploration of the effects of metabolic and physiological activity on the pore space of plant parenchyma tissue.

BTD anormais, accruals discricionários e qualidade dos accruals em empresas de capital aberto listadas no Brasil

Este estudo teve como objetivo analisar as implicações das BTD, decorrentes das práticas de gerenciamentos dos resultados contábeis e tributários, nos accruals discricionários e na qualidade dos accruals em empresas de capital aberto listadas no Brasil. Metodologicamente, considerou-se uma amostra de 290 empresas e o período de 2002 a 2015, com a apuração de uma medida de BTD Anormais para o contexto brasileiro. Em seguida, associou-se as BTD Anormais com proxies de gerenciamento de resultados, que foram obtidas de Dechow, Sloan e Sweeny (1995) e Dechow e Dichev (2002). Houve a utilização da técnica de dados em painel dinâmico com momentos generalizados (GMM). Os resultados indicaram que as BTD Anormais são decorrentes do gerenciamento de resultados por accruals discricionários e pela diminuição na qualidade dos accruals. Com isso, concluiu-se e contribuiu-se com a literatura que há tendências de assimetria informacional nas BTD, principalmente em seus componentes discricionários, diminuindo a qualidade dos lucros.

A time-space dynamic panel data model with spatial moving average errors

This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic panel data model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi et al. (2014) and Fingleton (2008a,b). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run effects and evaluate the predictive efficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001–2012, with the last two years reserved for prediction.

A Time-Space Dynamic Panel Data Model with Spatial Moving Average Errors

This paper focuses on the estimation and predictive performance of several estimators for the time-space dynamic panel data model with Spatial Moving Average Random Effects (SMA-RE) structure of the disturbances. A dynamic spatial Generalized Moments (GM) estimator is proposed which combines the approaches proposed by Baltagi, Fingleton and Pirotte (2014) and Fingleton (2008). The main idea is to mix non-spatial and spatial instruments to obtain consistent estimates of the parameters. Then, a forecasting approach is proposed and a linear predictor is derived. Using Monte Carlo simulations, we compare the short-run and long-run effects and evaluate the predictive efficiencies of optimal and various suboptimal predictors using the Root Mean Square Error (RMSE) criterion. Last, our approach is illustrated by an application in geographical economics which studies the employment levels across 255 NUTS regions of the EU over the period 2001–2012, with the last two years reserved for prediction.

An Application of Generalized Moments Method to Examine the Management Behavior during Peak Season A Study in Islamic Micro Finance Industry

In the current highly competitive finance industry, it is important that any financial institution to develop a proper strategy to compete in the market. More specifically, the finance managers need to have an appropriate liquidity and stability strategy during the peak season where their customers have a high demand of cash. Finding such a strategy is challenging for micro finance institution due to their limited resources, in terms of finance and human resources. This study is aimed to (1) analyze the impact of liquidity risk to the stability of micro finance institutions and to (2) assess the relationship between liquidity risk and credit risk in the period of peak season by using Generalized Moments Method. The focus of this study is Islamic micro finance institutions in West Java for the period 2012-2017. Using monthly data, this study reveals that the risk of liquidity has a negative effect on the level of stability of micro finance institutions at peak season and has a positive influence on the level of stability at off peak season. Further, the study finds that the relationship between liquidity risk and credit risk is significant during the period of peak season and off peak season period. Therefore, it can be concluded that in general credit risk impacts on liquidity risk. The findings of this study provide significant contributions in terms of enlarging our understanding on the management behavior related to institution liquidity and stability during the peak and non-peak season. From managerial perspective, this study helps the Islamic micro finance institution to remain stable and competitive during the peak season.

A multidimensional spatial lag panel data model with spatial moving average nested random effects errors

This paper focuses on a three-dimensional model that combines two different types of spatial interaction effects, i.e. endogenous interaction effects via a spatial lag on the dependent variable and interaction effects among the disturbances via a spatial moving average (SMA) nested random effects errors. A three-stage procedure is proposed to estimate the parameters. In a first stage, the spatial lag panel data model is estimated using an instrumental variable (IV) estimator. In a second stage, a generalized moments (GM) approach is developed to estimate the SMA parameter and the variance components of the disturbance process using IV residuals from the first stage. In a third stage, to purge the equation of the specific structure of the disturbances a Cochrane–Orcutt-type transformation is applied combined with the IV principle. This leads to the GM spatial IV estimator and the regression parameter estimates. Monte Carlo simulations show that our estimators are not very different in terms of root mean square error from those produced by maximum likelihood. The approach is applied to European Union regional employment data for regions nested within countries.

On the extreme points of moments sets

Necessary and sufficient conditions for a measure to be an extreme point of the set of measures on a given measurable space with prescribed generalized moments are given, as well as an application to extremal problems over such moment sets; these conditions are expressed in terms of atomic partitions of the measurable space. It is also shown that every such extreme measure can be adequately represented by a linear combination of k Dirac probability measures with nonnegative coefficients, where k is the number of restrictions on moments; moreover, when the measurable space has appropriate topological properties, the phrase “can be adequately represented by” here can be replaced simply by “is”. Applications to specific extremal problems are also given, including an exact lower bound on the exponential moments of truncated random variables, exact lower bounds on generalized moments of the interarrival distribution in queuing systems, and probability measures on product spaces with prescribed generalized marginal moments.

Estimation of Panel Model with Spatial Autoregressive Error and Common Factors

This study explores the estimation of a panel model that combines multifactor error with spatial correlation. On the basis of common correlated effects pooled (CCEP) estimator (Pesaran in Econometrica 74:967---1012, 2006), the generalized moments (GM) procedure suggested by Kelejian and Prucha (Int Econ Rev 40:509---533, 1999) is employed to estimate the spatial autoregressive parameters. These estimators are then used to define feasible generalized least squares (FGLS) procedures for the regression parameters. Given N and T $$\longrightarrow \infty $$?? (jointly), this study provides formal large sample results on the consistency of the proposed GM procedures, as well as the consistency and asymptotic normality of the proposed feasible generalized least squares (FGLS). It is proved that FGLS is more efficient than CCEP. The small sample properties of the various estimators are investigated by Monte Carlo experiments, which confirmed the theoretical conclusions. Results demonstrate that the popular spatial correlation analysis used in previous empirical literature may be misleading because it neglects common factors.

Fixed Effects and Random Effects Estimation of Higher-order Spatial Autoregressive Models with Spatial Autoregressive and Heteroscedastic Disturbances

This paper develops a unified framework for fixed effects (FE) and random effects (RE) estimation of higher-order spatial autoregressive panel data models with spatial autoregressive disturbances and heteroscedasticity of unknown form in the idiosyncratic error component. We derive the moment conditions and optimal weighting matrix without distributional assumptions for a generalized moments (GM) estimation procedure of the spatial autoregressive parameters of the disturbance process and define both an RE and an FE spatial generalized two-stage least squares estimator for the regression parameters of the model. We prove consistency of the proposed estimators and derive their joint asymptotic distribution, which is robust to heteroscedasticity of unknown form in the idiosyncratic error component. Finally, we derive a robust Hausman test of the spatial random against the spatial FE model.