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Showing 1 to 6 of 6 for “"GMM model"”.
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El efecto de la inclusión financiera en el crecimiento económico: un análisis para países desarrollados y en vías de desarrollo
… 2008 to 2019. A Generalized Method of Moments (GMM) model was used to explore the relationship of three representative dimensions of financial inclusion, through which it was found that an increase of 1% in the percentage of current loans could generate an average increase of 0.64% in the growth …
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General method of moments bias and specification tests for quantile regression
… 1: This chapter looks at a dynamic panel data model with fixed effects. Estimating the model with GMM is consistent but suffers from small sample bias. We apply Helmert's transformation to the model, assume that error terms and nuisance parameters are homoskedastic and independent across …
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Other people's money: does external debt improve economic growth performance of Frontier market economies?
… using the system Generalized Methods of Moments (GMM) model to test for a linear relationship as well as an inverted U-shape curve between external debt and economic growth. An inverted U-shape curve implies that a threshold point exists which signals a point beyond which an economy becomes …
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Foreign aid and economic performance during crises in Sub-Saharan Africa
… 2001 to 2021. The generalised method of moments (GMM) model was used to investigate the relationship between foreign aid and economic performance. The investigation of the relationship between foreign aid and economic performance was done for the whole SSA region as well as the four sub-regions. …
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Financial Development and Economic Growth
… Bover (1995), this paper utilizes the panel data model to discuss financial development and economic growth. The results point out that bank credit in total GDP and capitalization of stock market show significantly positive related with economic growth. Therefore, we can conclude that the economic …
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Mathematical Study on the Expressive Power of Machine Learning and Applications in Optimal Filtering Problems
… practical expressive power of machine learning models as general function approximators and their practical applications in optimal filtering problems. The practical expressive power of these models depends on the choice of \textit{model structure} (deep or shallow), which sets the upper limit …