The imperative of achieving financial stability has transcended national boundaries, necessitating heightened attention from both researchers and policymakers. Consequently, this article delves into an examination of the impact of government debt and public debt on financial development within the context of Iraq. The study employs monetary policy, interest rate, inflation, and population growth as control variables to prognosticate financial development. Utilizing data extracted from the World Development Indicators (WDI) spanning the period from 1995 to 2022, the study employs the dynamic autoregressive distributed lag (DARDL) approach to scrutinize the associations under investigation. The findings underscore a negative association between government debt and public debt, while revealing a positive association between monetary policy, interest rate, inflation, population growth, and financial development. Consequently, the study provides valuable insights to policymakers, offering guidance for the formulation of regulations aimed at enhancing financial development through the mitigation of indebtedness.
The main problem when dealing with fuzzy data variables is that it cannot be formed by a model that represents the data through the method of Fuzzy Least Squares Estimator (FLSE) which gives false estimates of the invalidity of the method in the case of the existence of the problem of multicollinearity. To overcome this problem, the Fuzzy Bridge Regression Estimator (FBRE) Method was relied upon to estimate a fuzzy linear regression model by triangular fuzzy numbers. Moreover, the detection of the problem of multicollinearity in the fuzzy data can be done by using Variance Inflation Factor when the inputs variable of the model crisp, output variable, and parameters are fuzzed. The results were compared usin
... Show MoreThe complexity and variety of language included in policy and academic documents make the automatic classification of research papers based on the United Nations Sustainable Development Goals (SDGs) somewhat difficult. Using both pre-trained and contextual word embeddings to increase semantic understanding, this study presents a complete deep learning pipeline combining Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) architectures which aims primarily to improve the comprehensibility and accuracy of SDG text classification, thereby enabling more effective policy monitoring and research evaluation. Successful document representation via Global Vector (GloVe), Bidirectional Encoder Representations from Tra
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