Time series analysis is crucial for modeling and forecasting diverse real-world phenomena. Traditional models typically assume continuous-valued data; however, many applications involve integer-valued series, often including negative integers. This paper introduces an approach that combines copula theory with the bivariate Skellam distribution to handle such integer-valued data effectively. Copulas are widely recognized for capturing complex dependencies among variables. By integrating copulas, our proposed method respects integer constraints while modeling positive, negative, and temporal dependencies accurately. Through simulation and an empirical study on a real-life example, we demonstrate that our class of models performs well. This approach has broad applicability in areas such as finance, epidemiology, and environmental science, where modeling series with integer values, both positive and negative, is essential.

This study has two main objectives, first, to investigate the unconditional effects of research productivity on economic complexity, and second, to examine the synergistic relationship between financial development and research productivity in bolstering Africa’s economic complexity. The estimation framework is designed to analyze how research productivity (i.e., quantity and quality of scholarly work produced) is moderated by both financial depth and the size of financial intermediaries, thereby positively influencing economic complexity. The analysis employs the dynamic system generalized method of moments (GMM) and the static Fixed Effects estimation with Driscoll–Kraay standard errors (DK), using data from 36 African economies over the period 2010–2023. The baseline estimation approach (i.e., System GMM) is designed to ensure valid model selection, mitigate variable omission bias, and prevent instrument proliferation. The study reveals several key findings: First, financial development consistently moderates the negative impact of research productivity on economic complexity, emphasizing its role as a potent channel for enhancing economic complexity in Africa. Second, factors such as renewable energy consumption and ICT diffusion are critical in boosting the region’s economic complexity. The policy implications of these findings are also discussed in line with the Sustainable Development Goals (SDGs).