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Despite the known strengths of the reinsurance companies to generate immense profits, evidence from existing literature indicates that the future of the reinsurance companies needs to be more robust to economic deficiencies leading to underperformance. There are many possible factors behind this. However, this study aimed to determine the relationship between macroeconomic factors and the profitability of African reinsurance companies with a Generalized Method of Moments (GMM) model. The study used 121 listed reinsurance companies from 48 African countries using secondary data from year 2008 to 2019. A 1452 observation panel data set was analyzed using conventional least squares and two-step System GMM estimators. The study revealed that GDP, interest rate, and the exchange rate positively impact profitability. In contrast, the inflation rate and money supply revealed a negative and negligible impact on profitability. The input of this research resides in providing new evidence on the macroeconomic factors influencing the profitability of listed reinsurance companies in Africa.

In the arena of economic analysis, the wealth of a nation is getting more and more attention to be a better indicator to evaluate the status of an economy. This paper had studied the aggregate household wealth of different nations of the world, 106 countries, for the year 2009-2018. During these years, only two countries of the world, China and the USA have managed to increase their wealth tremendously over the last decade while others experienced a slow pace in the growth of wealth. To satisfy the query of how efficient these countries were in maximizing their wealth, a stochastic frontier approach (SFA) has been used to predict the technical efficiency dependent variable and then generalized methods of moments (GMM) and other models have been used to find out the determinants of this efficiency. The study had come up with the result that land, labor, and capital mainly contributed to the output of wealth maximization while past year level of efficiency, export, and import played the main roles in determining the wealth maximizing efficiency status of a nation. It is found that there is a negative relationship between past-year efficiency with current years and the more a country imports, the less efficient the country is while the more it exports, the more efficient the country is in maximizing wealth.

PurposeThe purpose of this paper is to empirically test the economic convergence that operate between five selected Asian countries (namely Thailand, Singapore, Malaysia, the Philippines and Indonesia). In particular, it seeks to investigate how increased economic integration has impacted the inter-country income levels among the five founding members of ASEAN.Design/methodology/approachA new Machine Learning (ML) approach is applied along with a panel data analysis (GMM), and the application of KOF Globalization Index.FindingsThe Generalized Method of Moments (GMM) results highlight that the endogenous growth theory seems to be supported for the selected Asian countries, indicating evidence of diverging forces resulting from unequal growth and polarization dynamics. Overcoming the technical issues raised by the econometric approach, the new ML algorithm brings contrasted but interesting results. Using the KOF Globalization Index, the authors confirm how the last phase of globalization set the conditions for an economic convergence among sample members.Originality/valueUsing the KOF Globalization Index, the authors confirm how the last phase of globalization set the conditions for an economic convergence among sample members. As a matter of fact, the new LSTM algorithm has provided consistent evidence supporting the existence of converging forces. In fact, the results highlighted the effectiveness of the experiments and the algorithm we chose. The high predictability of the authors' model and the absence of self-alignment in the values showed a convergence be-tween the economies.

While coping with nonsphericality of the disturbances, standard GMM suffers from a blind spot for exploiting the most effective instruments when these are obtained directly from unconditional rather than conditional moment assumptions. For instance, standard GMM counteracts that exogenous regressors are used as their own optimal instruments. This is easily seen after transmuting GMM for linear models into IV in terms of transformed variables. It is demonstrated that modified GMM (MGMM), exploiting straight-forward modifications of the instruments, can achieve substantial efficiency gains and bias reductions, even under mild heteroskedasticity. Feasible MGMM implementations and their standard er-ror estimates are examined and compared with standard GMM and IV for a range of typical models for cross-section data, both by simulation and by empirical illustration.

The final publication is available via https://doi.org/10.1109/ICFHR-2018.2018.00105 . ; MultiSpectral Imaging enhances the study of degraded historical documents. It allows for visualizing washed out or even invisible ink but also improves the automated analysis because of a denser spectral sampling. We present a new methodology for binarization of multispectral document images that groups spectral signatures of different sources by fitting two Gaussian Mixture Models (GMMs) with Expectation Maximization. Both GMMs assign cluster labels to the multispectral samples and the clustering results are combined for the identification of the handwriting regions. The method is evaluated on the ICDAR 2015 MS-TEx dataset. Results on this publicly available benchmarking set are encouraging. ; European Union's Horizon 2020