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It is shown in the literature that the Arellano–Bond type generalized method of moments (GMM) of dynamic panel models is asymptotically biased (e.g., Hsiao & Zhang, 2015; Hsiao & Zhou, 2017). To correct the asymptotical bias of Arellano–Bond GMM, the authors suggest to use the jackknife instrumental variables estimation (JIVE) and also show that the JIVE of Arellano–Bond GMM is indeed asymptotically unbiased. Monte Carlo studies are conducted to compare the performance of the JIVE as well as Arellano–Bond GMM for linear dynamic panels. The authors demonstrate that the reliability of statistical inference depends critically on whether an estimator is asymptotically unbiased or not.

The Kinect sensor released by Microsoft is well-known for its effectiveness on human gesture recognition. Gesture recognition by Kinect has been proved to be an efficient command operation and provides an additional human-computer interface in addition to the traditional speech recognition. For Kinect gesture recognition in the application of gesture command operations, recognition of the active user making the gesture command to Kinect will be an extremely crucial problem. The purpose of this paper is to propose a recognition method for recognizing the person identity of an active user using combined eigenspace and Gaussian mixture model (GMM) with Kinect-extracted action gesture features.

Several Kinect-derived gesture features will be explored for determining the effective pattern features in the active user recognition task. In this work, a separate Kinect-derived feature design for eigenspace recognition and GMM classification is presented for achieving the optimal performance of each individual classifier. In addition to Kinect-extracted feature designs for active user recognition, this study will further develop a combined recognition method, called combined eigenspace-GMM, which properly hybridizes the decision information of both the eigenspace and the GMM for making a more reliable user recognition result.

Active user recognition using an effective combination of eigenspace and GMM with well-developed active gesture features in Kinect-based active user recognition will have an outstanding performance on the recognition accuracy. The presented Kinect-based user recognition system using the presented approach will further have the competitive benefits of recognition on both gesture commands and providing users of gesture commands.

A hybridized scheme of eigenspace and GMM performs better than eigenspace-alone or GMM-alone on recognition accuracy of active user recognition; a separate Kinect-derived feature design for eigenspace recognition and GMM classification is presented for achieving the optimal performance of the individual classifier; combined eigenspace-GMM active user recognition belonging to model-based active user recognition design has a fine extension on increasing the recognition rate by adjusting recognition models.

This study aims to explore the relationship between tourism and women’s employment in 32 South American and Caribbean countries from 1996 to 2020.

In this paper, both static (fixed effects and random effects) and dynamic panel data models (system and differenced generalized method of moments) are used. In addition to gross domestic product, trade, education and urban population are also considered in this study.

According to the findings, a boost in tourism led to an increase in women’s engagement in the economy and service sectors. This paper also explores the efficiency of alternate methods to deal with various models of women labor force (WLF) involvement in various sectors. Women’s employment opportunities in the service sector expand as a result of tourism, but in the agricultural and industrial sectors, that employment opportunity is reduced.

This study investigated the impact of tourism on WLF participation and found that it had a significant impact. This study, on the other hand, specifically contributed to the tourism sector in some specific study areas, such as tourism and agriculture, service and industry sectors. This study also displays that female participation in South America and the Caribbean countries is increasing and women are shifting away from traditional economic sectors.

This is the pioneering study to discover tourism and female participation in employment in South American and Caribbean countries. The findings of this study have important implications for future studies and policy debates examining the consequence of the tourism industry on WLF.

This paper seeks to reconsider the Euler equation of the Consumption Capital Asset Pricing Model (CCAPM), to derive a regression‐based model to test it, and to present evidence that the model is consistent with reasonable values for the coefficient of relative risk aversion (CRRA). This runs contrary to the findings of the literature on the equity premium puzzle, but is in agreement with the literature that estimates the CRRA for the purpose of computing the social discount rate, and is in line with the research on labor supply. Tests based on General Method of Moments (GMM) for the same sample produce results that are extremely disparate and unstable. The paper aims to check and find support for the robustness of the regression‐based tests. Habit formation models are also to be evaluated with regression‐based and GMM tests. However, the validity of the regression‐based models depends critically on their functional forms.

The paper presents empirical evidence that the conventional use of GMM fails because of four pathological features of GMM that are referred to under the general caption of “weak identification”. In addition to GMM, the paper employs linear regression analysis to test the CCAPM, and it is found that the regression residuals follow well‐behaved distributional properties, making valid all statistical inferences, while GMM estimates are highly unstable.

Four unexpected findings are reported. The first is that the regression‐based models are consistent with reasonable values for the CRRA, i.e. estimates that are below 4. The second is that the regression‐based tests are robust, while the GMM‐based tests are not. The third is that regression‐based tests with habit formation depend crucially on the specification of the model. The fourth is that there is evidence that market stock returns are sensitive to both consumption and dividends. The author calls the latter “extra sensitivity of market stock returns”, and it is described as a new puzzle.

The regression‐based models of the CCAPM Euler equation are novel. The comparison between GMM and regression‐based models for the same sample is original. The regression‐based models with habit formation are new. The equity premium puzzle disappears because the estimates of the CRRA are reasonable. But another puzzle is documented, which is the “extra sensitivity of market stock returns” to consumption and dividends together.

Standard multivariate tests of mean variance efficiency (MVE) have been criticised on the grounds that they require regression residuals to have a multivariate normal distribution. Generally, the existing evidence suggests that the normality assumption is questionable, even for monthly returns. MacKinlay and Richardson (1991) developed a generalised method of moments (GMM) framework which provides tests which are valid under much weaker distributional assumptions. They examined monthly US data formed into size based portfolios, for mean‐variance efficiency relative to the Sharpe‐Lintner CAPM. They found that inferences regarding mean‐variance efficiency can be sensitive to the test considered. In this paper we further investigate their GMM tests using monthly Australian data over the period 1974 to 1994. We extend upon their analysis to consider an alternative version of their GMM test and also to examine a zero‐beta version of the CAPM. Similar to the US case, our results also indicate sensitivity of inferences to the tests used. Finally, while we find that the GMM tests generally provide rejection of mean‐variance efficiency, tests involving the zero‐beta CAPM, particularly when a value‐weighted market index is used, prove less prone to rejection.

Owing to the excellent performance, giant magnetostrictive materials (GMMs) are widely used in many engineering fields. The dynamic Jiles–Atherton (J-A) model, derived from physical mechanism, is often used to describe the hysteresis characteristics of GMM. However, this model, despite cited by many different literature studies, seems not to possess unique expressions, which may cause great trouble to the subsequent application. This paper aims to provide the rational expressions of the dynamic J-A model and propose a numerical computation scheme to obtain the model results with high accuracy and fast speed.

This paper analyzes different published papers and provides a reasonable form of the dynamic J-A model based on functional properties and physical explanations. Then, a numerical computation scheme, combining the Newton method and the explicit Adams method, is designed to solve the modified model. In addition, the error source and transmission path of the numerical solution are investigated, and the influence of model parameters on the calculation error is explored. Finally, some attempts are made to study the influence of numerical scheme parameters on the accuracy and time of the computation process. Subsequently, an optimization procedure is proposed.

A rational form of the dynamic J-A model is concluded in this paper. Using the proposed numerical calculation scheme, the maximum calculation error, while computing the modified model, can remain below 2 A/m under different model parameter combinations, and the computation time is always less than 0.5 s. After optimization, the calculation speed can be enhanced with the computation accuracy guaranteed.

To the best of the authors’ knowledge, this paper is the first one trying to provide a rational form of the dynamic J-A model among different citations. No other research studies focus on designing a detailed computation scheme targeting the fast and accurate calculation of this model as well. And the performance of the proposed calculation method is validated in different conditions.

For the large-scale power grid monitoring system equipment, its working environment is increasingly complex and the probability of fault or failure of the monitoring system is gradually increasing. This paper proposes a fault classification algorithm based on Gaussian mixture model (GMM), which can complete the automatic classification of fault and the elimination of fault sources in the monitoring system.

The algorithm first defines the GMM and obtains the detection value of the fault classification through a method based on the causal Mason Young Tracy (MYT) decomposition under each normal distribution in the GMM. Then, the weight value of GMM is used to calculate weighted classification value of fault detection and separation, and by comparing the actual control limits with the classification result of GMM, the fault classification results are obtained.

The experiment on the defined non-thermostatic continuous stirred-tank reactor model shows that the algorithm proposed in this paper is superior to the traditional algorithm based on the causal MYT decomposition in fault detection and fault separation.

The proposed algorithm fundamentally solves the problem of fault detection and fault separation in large-scale systems and provides support for troubleshooting and identifying fault sources.

This study aims to find whether the Indian private corporate sector follow stable cash dividend policies, whether dividends smoothen earnings, estimate the implicit target dividend ratio, and examine the determinants along with speed of adjustment of dividends towards a long run target ratio.

The study uses the instrumental variable (IV) approach for dynamic panel data for 1971‐2010 periods controlling for economic reforms. The GMM‐in‐levels model, GMM‐in‐first‐differences and GMM‐in‐systems are alternatively estimated to include other lag structures.

In the post‐reform period lower dividends are consistent with rapid growth in the economic environment and the tendency to smoothen dividends has considerably decreased over time. The estimated model suggests dividends substitute for less opportunity for internal growth and increased general likening to relatively retain their earnings and finance their growth, unlike the past.

Limitation to capture substitution, ownership and self selection effects stems up from data as the Annual Studies RBI does not include such variables, does not capture qualitative data and disallows identification of the firm.

The paper documents long run trends and inter‐temporal dividend patterns controlling economic reforms for a relatively larger number of public limited firms nearing four decades for an emerging economy.

This is a first attempt to take a holistic view of dividend using rich set of unexplored dynamic panel data on Indian firms controlling for reforms using contemporary econometric models and analyzes issues relating determinants, smoothening and stability of the corporate dividend structure.

The purpose of this paper is to carry out a proof‐of‐concept experiment on positioning control of a smart journal bearing using giant magnetostrictive material (GMM)‐based actuators.

In the system, a journal bearing integrated with GMM actuators is actively controlled using self‐tuning P control under PID algorithm.

With the aid of GMM actuators, the positioning performance of journal bearing system is enhanced. The results demonstrate that the smart bearing system is practical and that GMM is an excellent material for driving function.

The smart bearing system can be applied to other types of machinery if new calibrations are done on both bearing and actuators.

The results prove the possibility of applying the smart bearing system to demanding industrial usages (high load capacity, control ability and stability at high speeds).

The idea of using GMM for the smart journal bearing is original and has not been mentioned in past literature.

This article aims to investigate how institutional characteristics affect the level of financial development of economies collectively and compare between developed and undeveloped economies.

A dynamic panel with 131 countries, including developed and developing ones, was utilized; the estimators of the generalized method of moments system (GMM system) model were selected because they have econometric characteristics more suitable for analysis, providing superior statistical precision compared to traditional linear estimation methods.

The results from the full panel suggest that concrete and well-defined institutions are important for financial development, confirming previous research, with a more limited scope than the present work.

Limitations of this research include the availability of data for all countries worldwide, which would make the research broader and more complete.

A panel of countries was used, divided into developed and developing countries, to analyze the impact of institutional variables on the financial development of these countries, which is one of the differentiators of this work. Another differentiator of this research is the presentation of estimates in six different configurations, with emphasis on the GMM system model in one and two steps, allowing for comparison between results.