The complexity of financial development and economic growth nexus in Syria: A nonlinear modelling approach with artificial neural networks and NARDL model

Abstract

This study investigates the complex interaction between financial development (FD) and economic growth (EG) in Syria from 1980 to 2018 using advanced nonlinear modeling techniques including artificial neural network VAR models, nonlinear causality tests, and nonlinear autoregressive distributed lag (NARDL) models. The results indicate that linear models are inadequate to capture the data patterns, necessitating nonlinear approaches. The artificial neural network VAR model reveals a nonlinear connection between FD and EG. The nonlinear causality test confirms that FD causes EG in a nonlinear manner. The NARDL (1, 1, 0, 1, 1) model is selected based on Akaike information criterion and diagnostics. The findings show a long-run equilibrium and short-run dynamics between FD and EG in Syria. Moreover, positive changes in FD have stronger, more persistent effects on EG compared to negative changes, implying asymmetry. Additionally, the impact of FD on EG is nonlinear, varying with FD levels. These results support recent studies suggesting a nonlinear nexus between FD and EG. They also lend support to the finance-led growth theory while opposing the “too much finance harms growth” hypothesis. The study offers policy implications for Syria to create conditions conducive to positive FD shocks and adopt a long-term perspective regarding FD-EG policies.